Overview

Dataset statistics

Number of variables55
Number of observations1355602
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory568.8 MiB
Average record size in memory440.0 B

Variable types

Numeric19
Categorical36

Warnings

order_status has constant value "delivered" Constant
declared_monthly_revenue has constant value "0.0" Constant
review_id has a high cardinality: 4447 distinct values High cardinality
order_id has a high cardinality: 4453 distinct values High cardinality
review_creation_date has a high cardinality: 516 distinct values High cardinality
review_answer_timestamp has a high cardinality: 4447 distinct values High cardinality
customer_id has a high cardinality: 4453 distinct values High cardinality
order_purchase_timestamp has a high cardinality: 4441 distinct values High cardinality
order_approved_at has a high cardinality: 4428 distinct values High cardinality
order_delivered_carrier_date has a high cardinality: 4393 distinct values High cardinality
order_delivered_customer_date has a high cardinality: 4450 distinct values High cardinality
order_estimated_delivery_date has a high cardinality: 402 distinct values High cardinality
product_id has a high cardinality: 3481 distinct values High cardinality
shipping_limit_date has a high cardinality: 4446 distinct values High cardinality
product_category_name has a high cardinality: 66 distinct values High cardinality
seller_id has a high cardinality: 362 distinct values High cardinality
seller_city has a high cardinality: 163 distinct values High cardinality
geolocation_city has a high cardinality: 223 distinct values High cardinality
customer_unique_id has a high cardinality: 4322 distinct values High cardinality
customer_city has a high cardinality: 155 distinct values High cardinality
mql_id has a high cardinality: 362 distinct values High cardinality
won_date has a high cardinality: 354 distinct values High cardinality
first_contact_date has a high cardinality: 141 distinct values High cardinality
landing_page_id has a high cardinality: 70 distinct values High cardinality
seller_zip_code_prefix is highly correlated with geolocation_zip_code_prefix and 1 other fieldsHigh correlation
geolocation_zip_code_prefix is highly correlated with seller_zip_code_prefix and 1 other fieldsHigh correlation
customer_zip_code_prefix is highly correlated with seller_zip_code_prefix and 1 other fieldsHigh correlation
product_category_name is highly correlated with order_status and 1 other fieldsHigh correlation
sr_id is highly correlated with order_status and 1 other fieldsHigh correlation
origin is highly correlated with order_status and 1 other fieldsHigh correlation
sdr_id is highly correlated with order_status and 1 other fieldsHigh correlation
geolocation_state is highly correlated with order_status and 3 other fieldsHigh correlation
order_status is highly correlated with product_category_name and 14 other fieldsHigh correlation
lead_behaviour_profile is highly correlated with order_status and 1 other fieldsHigh correlation
review_score is highly correlated with order_status and 1 other fieldsHigh correlation
lead_type is highly correlated with order_status and 1 other fieldsHigh correlation
business_type is highly correlated with order_status and 1 other fieldsHigh correlation
payment_type is highly correlated with order_status and 1 other fieldsHigh correlation
business_segment is highly correlated with order_status and 1 other fieldsHigh correlation
landing_page_id is highly correlated with order_status and 1 other fieldsHigh correlation
declared_monthly_revenue is highly correlated with product_category_name and 14 other fieldsHigh correlation
seller_state is highly correlated with geolocation_state and 3 other fieldsHigh correlation
customer_state is highly correlated with geolocation_state and 3 other fieldsHigh correlation
df_index has unique values Unique

Reproduction

Analysis started2021-03-16 09:35:06.610312
Analysis finished2021-03-16 09:57:37.828281
Duration22 minutes and 31.22 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct1355602
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean755088.2972
Minimum0
Maximum1510941
Zeros1
Zeros (%)< 0.1%
Memory size10.3 MiB
2021-03-16T17:57:38.708799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78459.05
Q1377174.25
median754573.5
Q31125143.75
95-th percentile1434011.95
Maximum1510941
Range1510941
Interquartile range (IQR)747969.5

Descriptive statistics

Standard deviation434484.0444
Coefficient of variation (CV)0.5754082616
Kurtosis-1.19718681
Mean755088.2972
Median Absolute Deviation (MAD)374125.5
Skewness0.002141281909
Sum1.023599206 × 1012
Variance1.887763848 × 1011
MonotocityStrictly increasing
2021-03-16T17:57:38.950570image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
12894361
 
< 0.1%
12853381
 
< 0.1%
12791931
 
< 0.1%
12812401
 
< 0.1%
13078631
 
< 0.1%
13099101
 
< 0.1%
13037651
 
< 0.1%
12955691
 
< 0.1%
12976161
 
< 0.1%
Other values (1355592)1355592
> 99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
ValueCountFrequency (%)
15109411
< 0.1%
15109401
< 0.1%
15109391
< 0.1%
15109381
< 0.1%
15109371
< 0.1%

review_id
Categorical

HIGH CARDINALITY

Distinct4447
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
03129dea7c12fa5878b2e629ccdf2ce6
 
6860
ee4bc8e340e8648a44c2e33fee6b27e4
 
6706
6e2e708cf22ce71fc6c89bde34e36a06
 
5200
101953a8fbbf1cc53809d5615bbf893b
 
4896
647d8053175e712d435045a22ab48e48
 
4896
Other values (4442)
1327044 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6f78e9f489a355a65ff8df35a39d3753
2nd row6f78e9f489a355a65ff8df35a39d3753
3rd row6f78e9f489a355a65ff8df35a39d3753
4th row6f78e9f489a355a65ff8df35a39d3753
5th row6f78e9f489a355a65ff8df35a39d3753
ValueCountFrequency (%)
03129dea7c12fa5878b2e629ccdf2ce66860
 
0.5%
ee4bc8e340e8648a44c2e33fee6b27e46706
 
0.5%
6e2e708cf22ce71fc6c89bde34e36a065200
 
0.4%
101953a8fbbf1cc53809d5615bbf893b4896
 
0.4%
647d8053175e712d435045a22ab48e484896
 
0.4%
6532f3ce8359fe5f1f69a476974556c14340
 
0.3%
c0dd4ca18424520737d855ec6f1c17b04048
 
0.3%
3e2e445b1e2abdce0ff0925681c1806d4004
 
0.3%
63f52661d616df41166b2c916db628f64004
 
0.3%
73e75eb3d7ec3e9758f4edc2fe809fdc3942
 
0.3%
Other values (4437)1306706
96.4%
2021-03-16T17:57:39.470630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
03129dea7c12fa5878b2e629ccdf2ce66860
 
0.5%
ee4bc8e340e8648a44c2e33fee6b27e46706
 
0.5%
6e2e708cf22ce71fc6c89bde34e36a065200
 
0.4%
101953a8fbbf1cc53809d5615bbf893b4896
 
0.4%
647d8053175e712d435045a22ab48e484896
 
0.4%
6532f3ce8359fe5f1f69a476974556c14340
 
0.3%
c0dd4ca18424520737d855ec6f1c17b04048
 
0.3%
3e2e445b1e2abdce0ff0925681c1806d4004
 
0.3%
63f52661d616df41166b2c916db628f64004
 
0.3%
73e75eb3d7ec3e9758f4edc2fe809fdc3942
 
0.3%
Other values (4437)1306706
96.4%

Most occurring characters

ValueCountFrequency (%)
e2810100
 
6.5%
d2791610
 
6.4%
c2775680
 
6.4%
52745273
 
6.3%
12724780
 
6.3%
b2724401
 
6.3%
62722970
 
6.3%
42717301
 
6.3%
82716876
 
6.3%
32687421
 
6.2%
Other values (6)15962852
36.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26964444
62.2%
Lowercase Letter16414820
37.8%

Most frequent character per category

ValueCountFrequency (%)
52745273
10.2%
12724780
10.1%
62722970
10.1%
42717301
10.1%
82716876
10.1%
32687421
10.0%
72687171
10.0%
02675635
9.9%
22665241
9.9%
92621776
9.7%
ValueCountFrequency (%)
e2810100
17.1%
d2791610
17.0%
c2775680
16.9%
b2724401
16.6%
f2683048
16.3%
a2629981
16.0%

Most occurring scripts

ValueCountFrequency (%)
Common26964444
62.2%
Latin16414820
37.8%

Most frequent character per script

ValueCountFrequency (%)
52745273
10.2%
12724780
10.1%
62722970
10.1%
42717301
10.1%
82716876
10.1%
32687421
10.0%
72687171
10.0%
02675635
9.9%
22665241
9.9%
92621776
9.7%
ValueCountFrequency (%)
e2810100
17.1%
d2791610
17.0%
c2775680
16.9%
b2724401
16.6%
f2683048
16.3%
a2629981
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
e2810100
 
6.5%
d2791610
 
6.4%
c2775680
 
6.4%
52745273
 
6.3%
12724780
 
6.3%
b2724401
 
6.3%
62722970
 
6.3%
42717301
 
6.3%
82716876
 
6.3%
32687421
 
6.2%
Other values (6)15962852
36.8%

order_id
Categorical

HIGH CARDINALITY

Distinct4453
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
9bdc4d4c71aa1de4606060929dee888c
 
6860
73c8ab38f07dc94389065f7eba4f297a
 
6706
f60ce04ff8060152c83c7c97e246d6a8
 
5200
6a30ecf6e27a0c6c8373a4ffc57cb353
 
4896
3200257acc9dd36d1fa819e72fca7e59
 
4896
Other values (4448)
1327044 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcd07e3ceeebd529181ed30afdf30d43f
2nd rowcd07e3ceeebd529181ed30afdf30d43f
3rd rowcd07e3ceeebd529181ed30afdf30d43f
4th rowcd07e3ceeebd529181ed30afdf30d43f
5th rowcd07e3ceeebd529181ed30afdf30d43f
ValueCountFrequency (%)
9bdc4d4c71aa1de4606060929dee888c6860
 
0.5%
73c8ab38f07dc94389065f7eba4f297a6706
 
0.5%
f60ce04ff8060152c83c7c97e246d6a85200
 
0.4%
6a30ecf6e27a0c6c8373a4ffc57cb3534896
 
0.4%
3200257acc9dd36d1fa819e72fca7e594896
 
0.4%
cf287f639abd10b1630574c4a065d4f14896
 
0.4%
18f4178d9b3c33fa79c1a0d5eb2140234340
 
0.3%
be382a9e1ed25128148b97d6bfdb21af4048
 
0.3%
61edec4c7086a5383dc4f5e6fca69e9b4004
 
0.3%
3241baf723e29f140a6ff9082b7f9aca4004
 
0.3%
Other values (4443)1305752
96.3%
2021-03-16T17:57:39.992329image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9bdc4d4c71aa1de4606060929dee888c6860
 
0.5%
73c8ab38f07dc94389065f7eba4f297a6706
 
0.5%
f60ce04ff8060152c83c7c97e246d6a85200
 
0.4%
6a30ecf6e27a0c6c8373a4ffc57cb3534896
 
0.4%
3200257acc9dd36d1fa819e72fca7e594896
 
0.4%
cf287f639abd10b1630574c4a065d4f14896
 
0.4%
18f4178d9b3c33fa79c1a0d5eb2140234340
 
0.3%
be382a9e1ed25128148b97d6bfdb21af4048
 
0.3%
61edec4c7086a5383dc4f5e6fca69e9b4004
 
0.3%
3241baf723e29f140a6ff9082b7f9aca4004
 
0.3%
Other values (4443)1305752
96.3%

Most occurring characters

ValueCountFrequency (%)
82826922
 
6.5%
f2776056
 
6.4%
72761417
 
6.4%
a2758546
 
6.4%
92749210
 
6.3%
32742747
 
6.3%
42728055
 
6.3%
e2720770
 
6.3%
c2719793
 
6.3%
62719214
 
6.3%
Other values (6)15876534
36.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27014524
62.3%
Lowercase Letter16364740
37.7%

Most frequent character per category

ValueCountFrequency (%)
82826922
10.5%
72761417
10.2%
92749210
10.2%
32742747
10.2%
42728055
10.1%
62719214
10.1%
02659271
9.8%
22639420
9.8%
52610318
9.7%
12577950
9.5%
ValueCountFrequency (%)
f2776056
17.0%
a2758546
16.9%
e2720770
16.6%
c2719793
16.6%
d2715202
16.6%
b2674373
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common27014524
62.3%
Latin16364740
37.7%

Most frequent character per script

ValueCountFrequency (%)
82826922
10.5%
72761417
10.2%
92749210
10.2%
32742747
10.2%
42728055
10.1%
62719214
10.1%
02659271
9.8%
22639420
9.8%
52610318
9.7%
12577950
9.5%
ValueCountFrequency (%)
f2776056
17.0%
a2758546
16.9%
e2720770
16.6%
c2719793
16.6%
d2715202
16.6%
b2674373
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
82826922
 
6.5%
f2776056
 
6.4%
72761417
 
6.4%
a2758546
 
6.4%
92749210
 
6.3%
32742747
 
6.3%
42728055
 
6.3%
e2720770
 
6.3%
c2719793
 
6.3%
62719214
 
6.3%
Other values (6)15876534
36.6%

review_score
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
5
791670 
4
248565 
1
164032 
3
115980 
2
 
35355

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1355602
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5
ValueCountFrequency (%)
5791670
58.4%
4248565
 
18.3%
1164032
 
12.1%
3115980
 
8.6%
235355
 
2.6%
2021-03-16T17:57:40.486130image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-16T17:57:40.666775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
5791670
58.4%
4248565
 
18.3%
1164032
 
12.1%
3115980
 
8.6%
235355
 
2.6%

Most occurring characters

ValueCountFrequency (%)
5791670
58.4%
4248565
 
18.3%
1164032
 
12.1%
3115980
 
8.6%
235355
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1355602
100.0%

Most frequent character per category

ValueCountFrequency (%)
5791670
58.4%
4248565
 
18.3%
1164032
 
12.1%
3115980
 
8.6%
235355
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common1355602
100.0%

Most frequent character per script

ValueCountFrequency (%)
5791670
58.4%
4248565
 
18.3%
1164032
 
12.1%
3115980
 
8.6%
235355
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1355602
100.0%

Most frequent character per block

ValueCountFrequency (%)
5791670
58.4%
4248565
 
18.3%
1164032
 
12.1%
3115980
 
8.6%
235355
 
2.6%

review_creation_date
Categorical

HIGH CARDINALITY

Distinct516
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2017-12-29 00:00:00
 
12557
2017-12-05 00:00:00
 
12475
2017-12-19 00:00:00
 
10914
2018-08-31 00:00:00
 
10403
2018-03-24 00:00:00
 
10374
Other values (511)
1298879 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters25756438
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-05-04 00:00:00
2nd row2017-05-04 00:00:00
3rd row2017-05-04 00:00:00
4th row2017-05-04 00:00:00
5th row2017-05-04 00:00:00
ValueCountFrequency (%)
2017-12-29 00:00:0012557
 
0.9%
2017-12-05 00:00:0012475
 
0.9%
2017-12-19 00:00:0010914
 
0.8%
2018-08-31 00:00:0010403
 
0.8%
2018-03-24 00:00:0010374
 
0.8%
2018-08-28 00:00:0010303
 
0.8%
2018-05-15 00:00:0010134
 
0.7%
2017-11-28 00:00:009645
 
0.7%
2018-03-02 00:00:008934
 
0.7%
2018-05-19 00:00:008774
 
0.6%
Other values (506)1251089
92.3%
2021-03-16T17:57:41.140154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:001355020
50.0%
2017-12-2912557
 
0.5%
2017-12-0512475
 
0.5%
2017-12-1910914
 
0.4%
2018-08-3110403
 
0.4%
2018-03-2410374
 
0.4%
2018-08-2810303
 
0.4%
2018-05-1510134
 
0.4%
2017-11-289645
 
0.4%
2018-03-028934
 
0.3%
Other values (508)1260445
46.5%

Most occurring characters

ValueCountFrequency (%)
011141906
43.3%
-2711204
 
10.5%
:2711204
 
10.5%
12341255
 
9.1%
22227857
 
8.6%
1355602
 
5.3%
81076423
 
4.2%
7836964
 
3.2%
3323597
 
1.3%
5280153
 
1.1%
Other values (3)750273
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18978428
73.7%
Dash Punctuation2711204
 
10.5%
Other Punctuation2711204
 
10.5%
Space Separator1355602
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
011141906
58.7%
12341255
 
12.3%
22227857
 
11.7%
81076423
 
5.7%
7836964
 
4.4%
3323597
 
1.7%
5280153
 
1.5%
4278207
 
1.5%
6269057
 
1.4%
9203009
 
1.1%
ValueCountFrequency (%)
-2711204
100.0%
ValueCountFrequency (%)
1355602
100.0%
ValueCountFrequency (%)
:2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25756438
100.0%

Most frequent character per script

ValueCountFrequency (%)
011141906
43.3%
-2711204
 
10.5%
:2711204
 
10.5%
12341255
 
9.1%
22227857
 
8.6%
1355602
 
5.3%
81076423
 
4.2%
7836964
 
3.2%
3323597
 
1.3%
5280153
 
1.1%
Other values (3)750273
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII25756438
100.0%

Most frequent character per block

ValueCountFrequency (%)
011141906
43.3%
-2711204
 
10.5%
:2711204
 
10.5%
12341255
 
9.1%
22227857
 
8.6%
1355602
 
5.3%
81076423
 
4.2%
7836964
 
3.2%
3323597
 
1.3%
5280153
 
1.1%
Other values (3)750273
 
2.9%

review_answer_timestamp
Categorical

HIGH CARDINALITY

Distinct4447
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2018-03-03 00:44:54
 
6860
2017-12-31 12:08:24
 
6706
2017-12-06 00:41:56
 
5200
2018-02-19 00:00:01
 
4896
2018-04-01 02:28:01
 
4896
Other values (4442)
1327044 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters25756438
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-05-05 19:51:22
2nd row2017-05-05 19:51:22
3rd row2017-05-05 19:51:22
4th row2017-05-05 19:51:22
5th row2017-05-05 19:51:22
ValueCountFrequency (%)
2018-03-03 00:44:546860
 
0.5%
2017-12-31 12:08:246706
 
0.5%
2017-12-06 00:41:565200
 
0.4%
2018-02-19 00:00:014896
 
0.4%
2018-04-01 02:28:014896
 
0.4%
2018-05-15 22:41:084340
 
0.3%
2017-11-26 12:37:324048
 
0.3%
2018-06-13 11:37:464004
 
0.3%
2017-08-21 19:25:444004
 
0.3%
2017-11-29 09:46:493942
 
0.3%
Other values (4437)1306706
96.4%
2021-03-16T17:57:41.579510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-04-0113044
 
0.5%
2017-12-0813013
 
0.5%
2018-02-199107
 
0.3%
2018-08-319095
 
0.3%
2017-11-298700
 
0.3%
2017-08-028672
 
0.3%
2017-12-118648
 
0.3%
2017-12-318637
 
0.3%
2018-05-218051
 
0.3%
2018-03-038033
 
0.3%
Other values (4883)2616204
96.5%

Most occurring characters

ValueCountFrequency (%)
04355175
16.9%
14025212
15.6%
23435858
13.3%
-2711204
10.5%
:2711204
10.5%
81397261
 
5.4%
1355602
 
5.3%
31255115
 
4.9%
71138316
 
4.4%
51098048
 
4.3%
Other values (3)2273443
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18978428
73.7%
Dash Punctuation2711204
 
10.5%
Other Punctuation2711204
 
10.5%
Space Separator1355602
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
04355175
22.9%
14025212
21.2%
23435858
18.1%
81397261
 
7.4%
31255115
 
6.6%
71138316
 
6.0%
51098048
 
5.8%
41081885
 
5.7%
6637036
 
3.4%
9554522
 
2.9%
ValueCountFrequency (%)
-2711204
100.0%
ValueCountFrequency (%)
1355602
100.0%
ValueCountFrequency (%)
:2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25756438
100.0%

Most frequent character per script

ValueCountFrequency (%)
04355175
16.9%
14025212
15.6%
23435858
13.3%
-2711204
10.5%
:2711204
10.5%
81397261
 
5.4%
1355602
 
5.3%
31255115
 
4.9%
71138316
 
4.4%
51098048
 
4.3%
Other values (3)2273443
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII25756438
100.0%

Most frequent character per block

ValueCountFrequency (%)
04355175
16.9%
14025212
15.6%
23435858
13.3%
-2711204
10.5%
:2711204
10.5%
81397261
 
5.4%
1355602
 
5.3%
31255115
 
4.9%
71138316
 
4.4%
51098048
 
4.3%
Other values (3)2273443
8.8%

customer_id
Categorical

HIGH CARDINALITY

Distinct4453
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
a7693fba2ff9583c78751f2b66ecab9d
 
6860
d5f2b3f597c7ccafbb5cac0bcc3d6024
 
6706
78fc46047c4a639e81ff65f0396e02fe
 
5200
c8a84c4b2aea91e2cfd48a87ae2a8947
 
4896
a193aa8d905b8e2460a1f49e26caa4a9
 
4896
Other values (4448)
1327044 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3c96cfdac2bce85bd05840b62c0e2e8e
2nd row3c96cfdac2bce85bd05840b62c0e2e8e
3rd row3c96cfdac2bce85bd05840b62c0e2e8e
4th row3c96cfdac2bce85bd05840b62c0e2e8e
5th row3c96cfdac2bce85bd05840b62c0e2e8e
ValueCountFrequency (%)
a7693fba2ff9583c78751f2b66ecab9d6860
 
0.5%
d5f2b3f597c7ccafbb5cac0bcc3d60246706
 
0.5%
78fc46047c4a639e81ff65f0396e02fe5200
 
0.4%
c8a84c4b2aea91e2cfd48a87ae2a89474896
 
0.4%
a193aa8d905b8e2460a1f49e26caa4a94896
 
0.4%
baa22cca29768548ef9cea07cf1057374896
 
0.4%
1f68e24da06f36cb8a5243e23c390bcf4340
 
0.3%
0c792d32a3251b4f69dae8646dfbedbc4048
 
0.3%
3e13d7562141418f00f431202390536c4004
 
0.3%
395ce4e9b5ad9165113d5321fcb7a8644004
 
0.3%
Other values (4443)1305752
96.3%
2021-03-16T17:57:42.424431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a7693fba2ff9583c78751f2b66ecab9d6860
 
0.5%
d5f2b3f597c7ccafbb5cac0bcc3d60246706
 
0.5%
78fc46047c4a639e81ff65f0396e02fe5200
 
0.4%
c8a84c4b2aea91e2cfd48a87ae2a89474896
 
0.4%
a193aa8d905b8e2460a1f49e26caa4a94896
 
0.4%
baa22cca29768548ef9cea07cf1057374896
 
0.4%
1f68e24da06f36cb8a5243e23c390bcf4340
 
0.3%
0c792d32a3251b4f69dae8646dfbedbc4048
 
0.3%
3e13d7562141418f00f431202390536c4004
 
0.3%
395ce4e9b5ad9165113d5321fcb7a8644004
 
0.3%
Other values (4443)1305752
96.3%

Most occurring characters

ValueCountFrequency (%)
f2770363
 
6.4%
a2762632
 
6.4%
62761626
 
6.4%
c2742849
 
6.3%
82739607
 
6.3%
92737291
 
6.3%
52722130
 
6.3%
12703052
 
6.2%
72699600
 
6.2%
e2698715
 
6.2%
Other values (6)16041399
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27068001
62.4%
Lowercase Letter16311263
37.6%

Most frequent character per category

ValueCountFrequency (%)
62761626
10.2%
82739607
10.1%
92737291
10.1%
52722130
10.1%
12703052
10.0%
72699600
10.0%
42691350
9.9%
02679661
9.9%
22676374
9.9%
32657310
9.8%
ValueCountFrequency (%)
f2770363
17.0%
a2762632
16.9%
c2742849
16.8%
e2698715
16.5%
b2676058
16.4%
d2660646
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common27068001
62.4%
Latin16311263
37.6%

Most frequent character per script

ValueCountFrequency (%)
62761626
10.2%
82739607
10.1%
92737291
10.1%
52722130
10.1%
12703052
10.0%
72699600
10.0%
42691350
9.9%
02679661
9.9%
22676374
9.9%
32657310
9.8%
ValueCountFrequency (%)
f2770363
17.0%
a2762632
16.9%
c2742849
16.8%
e2698715
16.5%
b2676058
16.4%
d2660646
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
f2770363
 
6.4%
a2762632
 
6.4%
62761626
 
6.4%
c2742849
 
6.3%
82739607
 
6.3%
92737291
 
6.3%
52722130
 
6.3%
12703052
 
6.2%
72699600
 
6.2%
e2698715
 
6.2%
Other values (6)16041399
37.0%

order_status
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
delivered
1355602 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters12200418
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered
ValueCountFrequency (%)
delivered1355602
100.0%
2021-03-16T17:57:42.893444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-16T17:57:43.060319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
delivered1355602
100.0%

Most occurring characters

ValueCountFrequency (%)
e4066806
33.3%
d2711204
22.2%
l1355602
 
11.1%
i1355602
 
11.1%
v1355602
 
11.1%
r1355602
 
11.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12200418
100.0%

Most frequent character per category

ValueCountFrequency (%)
e4066806
33.3%
d2711204
22.2%
l1355602
 
11.1%
i1355602
 
11.1%
v1355602
 
11.1%
r1355602
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin12200418
100.0%

Most frequent character per script

ValueCountFrequency (%)
e4066806
33.3%
d2711204
22.2%
l1355602
 
11.1%
i1355602
 
11.1%
v1355602
 
11.1%
r1355602
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12200418
100.0%

Most frequent character per block

ValueCountFrequency (%)
e4066806
33.3%
d2711204
22.2%
l1355602
 
11.1%
i1355602
 
11.1%
v1355602
 
11.1%
r1355602
 
11.1%

order_purchase_timestamp
Categorical

HIGH CARDINALITY

Distinct4441
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2018-02-21 11:45:07
 
6860
2017-12-13 14:21:15
 
6706
2017-11-28 22:24:18
 
5200
2018-02-12 18:04:28
 
4896
2017-11-24 12:58:53
 
4896
Other values (4436)
1327044 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters25756438
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-04-21 16:37:49
2nd row2017-04-21 16:37:49
3rd row2017-04-21 16:37:49
4th row2017-04-21 16:37:49
5th row2017-04-21 16:37:49
ValueCountFrequency (%)
2018-02-21 11:45:076860
 
0.5%
2017-12-13 14:21:156706
 
0.5%
2017-11-28 22:24:185200
 
0.4%
2018-02-12 18:04:284896
 
0.4%
2017-11-24 12:58:534896
 
0.4%
2018-03-25 14:29:454896
 
0.4%
2018-05-07 19:12:474340
 
0.3%
2017-11-04 17:29:154048
 
0.3%
2017-08-02 12:57:274004
 
0.3%
2018-06-06 15:11:414004
 
0.3%
Other values (4431)1305752
96.3%
2021-03-16T17:57:43.505596image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-2424513
 
0.9%
2018-02-129226
 
0.3%
2018-02-218743
 
0.3%
2017-11-288556
 
0.3%
2018-03-258437
 
0.3%
2017-11-268217
 
0.3%
2017-12-137996
 
0.3%
2018-03-267333
 
0.3%
2018-05-027289
 
0.3%
2017-11-257226
 
0.3%
Other values (4881)2613668
96.4%

Most occurring characters

ValueCountFrequency (%)
14245856
16.5%
04112655
16.0%
23381076
13.1%
-2711204
10.5%
:2711204
10.5%
81410641
 
5.5%
1355602
 
5.3%
71256160
 
4.9%
31148344
 
4.5%
51100203
 
4.3%
Other values (3)2323493
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18978428
73.7%
Dash Punctuation2711204
 
10.5%
Other Punctuation2711204
 
10.5%
Space Separator1355602
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
14245856
22.4%
04112655
21.7%
23381076
17.8%
81410641
 
7.4%
71256160
 
6.6%
31148344
 
6.1%
51100203
 
5.8%
41097614
 
5.8%
6658039
 
3.5%
9567840
 
3.0%
ValueCountFrequency (%)
-2711204
100.0%
ValueCountFrequency (%)
1355602
100.0%
ValueCountFrequency (%)
:2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25756438
100.0%

Most frequent character per script

ValueCountFrequency (%)
14245856
16.5%
04112655
16.0%
23381076
13.1%
-2711204
10.5%
:2711204
10.5%
81410641
 
5.5%
1355602
 
5.3%
71256160
 
4.9%
31148344
 
4.5%
51100203
 
4.3%
Other values (3)2323493
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25756438
100.0%

Most frequent character per block

ValueCountFrequency (%)
14245856
16.5%
04112655
16.0%
23381076
13.1%
-2711204
10.5%
:2711204
10.5%
81410641
 
5.5%
1355602
 
5.3%
71256160
 
4.9%
31148344
 
4.5%
51100203
 
4.3%
Other values (3)2323493
9.0%

order_approved_at
Categorical

HIGH CARDINALITY

Distinct4428
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2018-02-22 11:48:42
 
6860
2017-12-15 02:30:41
 
6706
2017-11-28 22:31:34
 
5200
2017-11-24 15:13:44
 
4896
2018-02-12 18:10:32
 
4896
Other values (4423)
1327044 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters25756438
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-04-21 16:50:18
2nd row2017-04-21 16:50:18
3rd row2017-04-21 16:50:18
4th row2017-04-21 16:50:18
5th row2017-04-21 16:50:18
ValueCountFrequency (%)
2018-02-22 11:48:426860
 
0.5%
2017-12-15 02:30:416706
 
0.5%
2017-11-28 22:31:345200
 
0.4%
2017-11-24 15:13:444896
 
0.4%
2018-02-12 18:10:324896
 
0.4%
2018-03-25 14:48:014896
 
0.4%
2018-05-07 19:30:084340
 
0.3%
2017-11-07 16:31:174048
 
0.3%
2017-08-02 13:10:104004
 
0.3%
2018-06-06 15:34:234004
 
0.3%
Other values (4418)1305752
96.3%
2021-03-16T17:57:44.028950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-2419456
 
0.7%
2018-04-2414578
 
0.5%
2018-02-2213596
 
0.5%
2017-11-2510409
 
0.4%
2017-11-2810329
 
0.4%
2018-05-0710181
 
0.4%
2018-07-059136
 
0.3%
2018-04-108773
 
0.3%
2017-12-158753
 
0.3%
2018-02-127901
 
0.3%
Other values (4645)2598092
95.8%

Most occurring characters

ValueCountFrequency (%)
04344463
16.9%
14187221
16.3%
23357970
13.0%
-2711204
10.5%
:2711204
10.5%
81359153
 
5.3%
1355602
 
5.3%
51281655
 
5.0%
31227669
 
4.8%
71180395
 
4.6%
Other values (3)2039902
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18978428
73.7%
Dash Punctuation2711204
 
10.5%
Other Punctuation2711204
 
10.5%
Space Separator1355602
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
04344463
22.9%
14187221
22.1%
23357970
17.7%
81359153
 
7.2%
51281655
 
6.8%
31227669
 
6.5%
71180395
 
6.2%
4971587
 
5.1%
6563665
 
3.0%
9504650
 
2.7%
ValueCountFrequency (%)
-2711204
100.0%
ValueCountFrequency (%)
1355602
100.0%
ValueCountFrequency (%)
:2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25756438
100.0%

Most frequent character per script

ValueCountFrequency (%)
04344463
16.9%
14187221
16.3%
23357970
13.0%
-2711204
10.5%
:2711204
10.5%
81359153
 
5.3%
1355602
 
5.3%
51281655
 
5.0%
31227669
 
4.8%
71180395
 
4.6%
Other values (3)2039902
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII25756438
100.0%

Most frequent character per block

ValueCountFrequency (%)
04344463
16.9%
14187221
16.3%
23357970
13.0%
-2711204
10.5%
:2711204
10.5%
81359153
 
5.3%
1355602
 
5.3%
51281655
 
5.0%
31227669
 
4.8%
71180395
 
4.6%
Other values (3)2039902
7.9%

order_delivered_carrier_date
Categorical

HIGH CARDINALITY

Distinct4393
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2018-02-27 18:27:01
 
6860
2017-12-15 18:45:18
 
6706
2017-11-29 19:48:33
 
5200
2018-03-26 23:48:46
 
4896
2018-02-14 22:58:48
 
4896
Other values (4388)
1327044 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters25756438
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-04-24 17:44:09
2nd row2017-04-24 17:44:09
3rd row2017-04-24 17:44:09
4th row2017-04-24 17:44:09
5th row2017-04-24 17:44:09
ValueCountFrequency (%)
2018-02-27 18:27:016860
 
0.5%
2017-12-15 18:45:186706
 
0.5%
2017-11-29 19:48:335200
 
0.4%
2018-03-26 23:48:464896
 
0.4%
2018-02-14 22:58:484896
 
0.4%
2017-11-28 15:29:054896
 
0.4%
2018-05-08 14:41:004340
 
0.3%
2017-11-21 14:22:284048
 
0.3%
2018-06-07 12:20:004004
 
0.3%
2017-08-04 17:05:524004
 
0.3%
Other values (4383)1305752
96.3%
2021-03-16T17:57:44.533574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-2814589
 
0.5%
2018-02-2713278
 
0.5%
2017-11-2912949
 
0.5%
2018-05-0812854
 
0.5%
2018-01-2311341
 
0.4%
2018-03-2610388
 
0.4%
2018-03-209756
 
0.4%
2017-12-119751
 
0.4%
2017-12-019545
 
0.4%
2018-08-208860
 
0.3%
Other values (3957)2597893
95.8%

Most occurring characters

ValueCountFrequency (%)
04639863
18.0%
13993591
15.5%
23283230
12.7%
-2711204
10.5%
:2711204
10.5%
81466072
 
5.7%
1355602
 
5.3%
71237612
 
4.8%
31097428
 
4.3%
41063806
 
4.1%
Other values (3)2196826
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18978428
73.7%
Dash Punctuation2711204
 
10.5%
Other Punctuation2711204
 
10.5%
Space Separator1355602
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
04639863
24.4%
13993591
21.0%
23283230
17.3%
81466072
 
7.7%
71237612
 
6.5%
31097428
 
5.8%
41063806
 
5.6%
51053206
 
5.5%
9574097
 
3.0%
6569523
 
3.0%
ValueCountFrequency (%)
-2711204
100.0%
ValueCountFrequency (%)
1355602
100.0%
ValueCountFrequency (%)
:2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25756438
100.0%

Most frequent character per script

ValueCountFrequency (%)
04639863
18.0%
13993591
15.5%
23283230
12.7%
-2711204
10.5%
:2711204
10.5%
81466072
 
5.7%
1355602
 
5.3%
71237612
 
4.8%
31097428
 
4.3%
41063806
 
4.1%
Other values (3)2196826
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII25756438
100.0%

Most frequent character per block

ValueCountFrequency (%)
04639863
18.0%
13993591
15.5%
23283230
12.7%
-2711204
10.5%
:2711204
10.5%
81466072
 
5.7%
1355602
 
5.3%
71237612
 
4.8%
31097428
 
4.3%
41063806
 
4.1%
Other values (3)2196826
8.5%

order_delivered_customer_date
Categorical

HIGH CARDINALITY

Distinct4450
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2018-03-01 20:47:01
 
6860
2017-12-28 09:05:34
 
6706
2017-12-04 22:22:20
 
5200
2017-12-04 20:33:01
 
4896
2018-03-30 19:28:53
 
4896
Other values (4445)
1327044 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters25756438
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-05-03 14:32:54
2nd row2017-05-03 14:32:54
3rd row2017-05-03 14:32:54
4th row2017-05-03 14:32:54
5th row2017-05-03 14:32:54
ValueCountFrequency (%)
2018-03-01 20:47:016860
 
0.5%
2017-12-28 09:05:346706
 
0.5%
2017-12-04 22:22:205200
 
0.4%
2017-12-04 20:33:014896
 
0.4%
2018-03-30 19:28:534896
 
0.4%
2018-02-16 22:32:514896
 
0.4%
2018-05-14 16:48:434340
 
0.3%
2017-11-23 20:28:464048
 
0.3%
2017-08-14 19:36:594004
 
0.3%
2018-06-11 22:11:144004
 
0.3%
Other values (4440)1305752
96.3%
2021-03-16T17:57:45.015649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-12-0413699
 
0.5%
2017-12-2813125
 
0.5%
2018-03-2311331
 
0.4%
2018-03-0110884
 
0.4%
2018-08-3010492
 
0.4%
2018-08-2710179
 
0.4%
2017-11-279679
 
0.4%
2018-05-149631
 
0.4%
2018-03-298803
 
0.3%
2017-06-198526
 
0.3%
Other values (4692)2604855
96.1%

Most occurring characters

ValueCountFrequency (%)
03959545
15.4%
13938351
15.3%
23487113
13.5%
-2711204
10.5%
:2711204
10.5%
81594312
6.2%
1355602
 
5.3%
31268274
 
4.9%
71244552
 
4.8%
41185315
 
4.6%
Other values (3)2300966
8.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18978428
73.7%
Dash Punctuation2711204
 
10.5%
Other Punctuation2711204
 
10.5%
Space Separator1355602
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
03959545
20.9%
13938351
20.8%
23487113
18.4%
81594312
8.4%
31268274
 
6.7%
71244552
 
6.6%
41185315
 
6.2%
51059017
 
5.6%
6677180
 
3.6%
9564769
 
3.0%
ValueCountFrequency (%)
-2711204
100.0%
ValueCountFrequency (%)
1355602
100.0%
ValueCountFrequency (%)
:2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25756438
100.0%

Most frequent character per script

ValueCountFrequency (%)
03959545
15.4%
13938351
15.3%
23487113
13.5%
-2711204
10.5%
:2711204
10.5%
81594312
6.2%
1355602
 
5.3%
31268274
 
4.9%
71244552
 
4.8%
41185315
 
4.6%
Other values (3)2300966
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII25756438
100.0%

Most frequent character per block

ValueCountFrequency (%)
03959545
15.4%
13938351
15.3%
23487113
13.5%
-2711204
10.5%
:2711204
10.5%
81594312
6.2%
1355602
 
5.3%
31268274
 
4.9%
71244552
 
4.8%
41185315
 
4.6%
Other values (3)2300966
8.9%

order_estimated_delivery_date
Categorical

HIGH CARDINALITY

Distinct402
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2017-12-18 00:00:00
 
13447
2017-12-27 00:00:00
 
12821
2018-03-07 00:00:00
 
12557
2018-04-05 00:00:00
 
12457
2018-02-09 00:00:00
 
11058
Other values (397)
1293262 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters25756438
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-05-23 00:00:00
2nd row2017-05-23 00:00:00
3rd row2017-05-23 00:00:00
4th row2017-05-23 00:00:00
5th row2017-05-23 00:00:00
ValueCountFrequency (%)
2017-12-18 00:00:0013447
 
1.0%
2017-12-27 00:00:0012821
 
0.9%
2018-03-07 00:00:0012557
 
0.9%
2018-04-05 00:00:0012457
 
0.9%
2018-02-09 00:00:0011058
 
0.8%
2018-07-12 00:00:0010933
 
0.8%
2017-12-20 00:00:0010193
 
0.8%
2018-04-20 00:00:009964
 
0.7%
2018-07-27 00:00:009869
 
0.7%
2018-01-08 00:00:009496
 
0.7%
Other values (392)1242807
91.7%
2021-03-16T17:57:45.464648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:001355602
50.0%
2017-12-1813447
 
0.5%
2017-12-2712821
 
0.5%
2018-03-0712557
 
0.5%
2018-04-0512457
 
0.5%
2018-02-0911058
 
0.4%
2018-07-1210933
 
0.4%
2017-12-2010193
 
0.4%
2018-04-209964
 
0.4%
2018-07-279869
 
0.4%
Other values (393)1252303
46.2%

Most occurring characters

ValueCountFrequency (%)
011222470
43.6%
-2711204
 
10.5%
:2711204
 
10.5%
12307097
 
9.0%
22115049
 
8.2%
1355602
 
5.3%
81110354
 
4.3%
7846508
 
3.3%
3353890
 
1.4%
5292293
 
1.1%
Other values (3)730767
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18978428
73.7%
Dash Punctuation2711204
 
10.5%
Other Punctuation2711204
 
10.5%
Space Separator1355602
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
011222470
59.1%
12307097
 
12.2%
22115049
 
11.1%
81110354
 
5.9%
7846508
 
4.5%
3353890
 
1.9%
5292293
 
1.5%
4257128
 
1.4%
6247836
 
1.3%
9225803
 
1.2%
ValueCountFrequency (%)
-2711204
100.0%
ValueCountFrequency (%)
1355602
100.0%
ValueCountFrequency (%)
:2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25756438
100.0%

Most frequent character per script

ValueCountFrequency (%)
011222470
43.6%
-2711204
 
10.5%
:2711204
 
10.5%
12307097
 
9.0%
22115049
 
8.2%
1355602
 
5.3%
81110354
 
4.3%
7846508
 
3.3%
3353890
 
1.4%
5292293
 
1.1%
Other values (3)730767
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII25756438
100.0%

Most frequent character per block

ValueCountFrequency (%)
011222470
43.6%
-2711204
 
10.5%
:2711204
 
10.5%
12307097
 
9.0%
22115049
 
8.2%
1355602
 
5.3%
81110354
 
4.3%
7846508
 
3.3%
3353890
 
1.4%
5292293
 
1.1%
Other values (3)730767
 
2.8%

payment_sequential
Real number (ℝ≥0)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.066447232
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:45.655177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum9
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.422129206
Coefficient of variation (CV)0.3958275604
Kurtosis126.7325389
Mean1.066447232
Median Absolute Deviation (MAD)0
Skewness9.970571974
Sum1445678
Variance0.1781930665
MonotocityNot monotonic
2021-03-16T17:57:45.831665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11301470
96.0%
238191
 
2.8%
37493
 
0.6%
43331
 
0.2%
51721
 
0.1%
61668
 
0.1%
7852
 
0.1%
8438
 
< 0.1%
9438
 
< 0.1%
ValueCountFrequency (%)
11301470
96.0%
238191
 
2.8%
37493
 
0.6%
43331
 
0.2%
51721
 
0.1%
ValueCountFrequency (%)
9438
 
< 0.1%
8438
 
< 0.1%
7852
0.1%
61668
0.1%
51721
0.1%

payment_type
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
credit_card
986770 
boleto
287254 
voucher
 
66563
debit_card
 
15015

Length

Max length11
Median length11
Mean length9.733007918
Min length6

Characters and Unicode

Total characters13194085
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcredit_card
2nd rowcredit_card
3rd rowcredit_card
4th rowcredit_card
5th rowcredit_card
ValueCountFrequency (%)
credit_card986770
72.8%
boleto287254
 
21.2%
voucher66563
 
4.9%
debit_card15015
 
1.1%
2021-03-16T17:57:46.227606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-16T17:57:46.524812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
credit_card986770
72.8%
boleto287254
 
21.2%
voucher66563
 
4.9%
debit_card15015
 
1.1%

Most occurring characters

ValueCountFrequency (%)
c2055118
15.6%
r2055118
15.6%
d2003570
15.2%
e1355602
10.3%
t1289039
9.8%
i1001785
7.6%
_1001785
7.6%
a1001785
7.6%
o641071
 
4.9%
b302269
 
2.3%
Other values (4)486943
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12192300
92.4%
Connector Punctuation1001785
 
7.6%

Most frequent character per category

ValueCountFrequency (%)
c2055118
16.9%
r2055118
16.9%
d2003570
16.4%
e1355602
11.1%
t1289039
10.6%
i1001785
8.2%
a1001785
8.2%
o641071
 
5.3%
b302269
 
2.5%
l287254
 
2.4%
Other values (3)199689
 
1.6%
ValueCountFrequency (%)
_1001785
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12192300
92.4%
Common1001785
 
7.6%

Most frequent character per script

ValueCountFrequency (%)
c2055118
16.9%
r2055118
16.9%
d2003570
16.4%
e1355602
11.1%
t1289039
10.6%
i1001785
8.2%
a1001785
8.2%
o641071
 
5.3%
b302269
 
2.5%
l287254
 
2.4%
Other values (3)199689
 
1.6%
ValueCountFrequency (%)
_1001785
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII13194085
100.0%

Most frequent character per block

ValueCountFrequency (%)
c2055118
15.6%
r2055118
15.6%
d2003570
15.2%
e1355602
10.3%
t1289039
9.8%
i1001785
7.6%
_1001785
7.6%
a1001785
7.6%
o641071
 
4.9%
b302269
 
2.3%
Other values (4)486943
 
3.7%

payment_installments
Real number (ℝ≥0)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.949155431
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:46.696352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q34
95-th percentile10
Maximum24
Range23
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.011912449
Coefficient of variation (CV)1.021279658
Kurtosis9.446204621
Mean2.949155431
Median Absolute Deviation (MAD)0
Skewness2.424979143
Sum3997881
Variance9.071616603
MonotocityNot monotonic
2021-03-16T17:57:46.900843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1699202
51.6%
2145613
 
10.7%
3125435
 
9.3%
486864
 
6.4%
582826
 
6.1%
1076917
 
5.7%
651274
 
3.8%
845401
 
3.3%
726132
 
1.9%
95766
 
0.4%
Other values (7)10172
 
0.8%
ValueCountFrequency (%)
1699202
51.6%
2145613
 
10.7%
3125435
 
9.3%
486864
 
6.4%
582826
 
6.1%
ValueCountFrequency (%)
245200
0.4%
20520
 
< 0.1%
18701
 
0.1%
15906
 
0.1%
14204
 
< 0.1%

payment_value
Real number (ℝ≥0)

Distinct3677
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean178.9453111
Minimum0.03
Maximum3666.42
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:47.100270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.03
5-th percentile25.88
Q160
median111.58
Q3194
95-th percentile547.96
Maximum3666.42
Range3666.39
Interquartile range (IQR)134

Descriptive statistics

Standard deviation245.3296562
Coefficient of variation (CV)1.370975605
Kurtosis31.20581507
Mean178.9453111
Median Absolute Deviation (MAD)60.77
Skewness4.721957009
Sum242578621.6
Variance60186.6402
MonotocityNot monotonic
2021-03-16T17:57:47.343620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
528.786860
 
0.5%
1014.026706
 
0.5%
1440.15200
 
0.4%
333.364896
 
0.4%
536.524896
 
0.4%
305.894340
 
0.3%
504188
 
0.3%
64080
 
0.3%
2039.24048
 
0.3%
392.624004
 
0.3%
Other values (3667)1306384
96.4%
ValueCountFrequency (%)
0.03200
 
< 0.1%
0.23965
0.1%
0.33408
< 0.1%
0.59143
 
< 0.1%
0.74217
 
< 0.1%
ValueCountFrequency (%)
3666.42186
 
< 0.1%
3005.06272
< 0.1%
2455.1264
 
< 0.1%
2322.32128
 
< 0.1%
2264.9506
< 0.1%

order_item_id
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.297953234
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:47.546079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile3
Maximum14
Range13
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.078089869
Coefficient of variation (CV)0.8306076372
Kurtosis50.76120715
Mean1.297953234
Median Absolute Deviation (MAD)0
Skewness6.322318931
Sum1759508
Variance1.162277765
MonotocityNot monotonic
2021-03-16T17:57:47.728590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
11159171
85.5%
2124627
 
9.2%
329641
 
2.2%
415882
 
1.2%
57078
 
0.5%
65584
 
0.4%
73021
 
0.2%
82722
 
0.2%
92073
 
0.2%
101635
 
0.1%
Other values (4)4168
 
0.3%
ValueCountFrequency (%)
11159171
85.5%
2124627
 
9.2%
329641
 
2.2%
415882
 
1.2%
57078
 
0.5%
ValueCountFrequency (%)
14969
0.1%
13969
0.1%
121115
0.1%
111115
0.1%
101635
0.1%

product_id
Categorical

HIGH CARDINALITY

Distinct3481
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
422879e10f46682990de24d770e7f83d
 
10692
44a5d24dd383324a421569ca697b13c2
 
6860
cec09725da5ed01471d9a505e7389d37
 
6551
389d119b48cf3043d311335e499d9c6b
 
6542
167b4b8c4bd0c401bea62f5e050d70a4
 
6363
Other values (3476)
1318594 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbe0dbdc3d67d55727a65d4cd696ca73c
2nd rowbe0dbdc3d67d55727a65d4cd696ca73c
3rd rowbe0dbdc3d67d55727a65d4cd696ca73c
4th rowbe0dbdc3d67d55727a65d4cd696ca73c
5th rowbe0dbdc3d67d55727a65d4cd696ca73c
ValueCountFrequency (%)
422879e10f46682990de24d770e7f83d10692
 
0.8%
44a5d24dd383324a421569ca697b13c26860
 
0.5%
cec09725da5ed01471d9a505e7389d376551
 
0.5%
389d119b48cf3043d311335e499d9c6b6542
 
0.5%
167b4b8c4bd0c401bea62f5e050d70a46363
 
0.5%
368c6c730842d78016ad823897a372db6225
 
0.5%
99a4788cb24856965c36a24e339b60585404
 
0.4%
165f86fe8b799a708a20ee4ba125c2895352
 
0.4%
dd8e3ce8409dea51c566f227a4fc34ba4896
 
0.4%
aca2eb7d00ea1a7b8ebd4e68314663af4561
 
0.3%
Other values (3471)1292156
95.3%
2021-03-16T17:57:48.161494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
422879e10f46682990de24d770e7f83d10692
 
0.8%
44a5d24dd383324a421569ca697b13c26860
 
0.5%
cec09725da5ed01471d9a505e7389d376551
 
0.5%
389d119b48cf3043d311335e499d9c6b6542
 
0.5%
167b4b8c4bd0c401bea62f5e050d70a46363
 
0.5%
368c6c730842d78016ad823897a372db6225
 
0.5%
99a4788cb24856965c36a24e339b60585404
 
0.4%
165f86fe8b799a708a20ee4ba125c2895352
 
0.4%
dd8e3ce8409dea51c566f227a4fc34ba4896
 
0.4%
aca2eb7d00ea1a7b8ebd4e68314663af4561
 
0.3%
Other values (3471)1292156
95.3%

Most occurring characters

ValueCountFrequency (%)
42795639
 
6.4%
32773388
 
6.4%
c2762014
 
6.4%
82751002
 
6.3%
a2744764
 
6.3%
02739763
 
6.3%
72732068
 
6.3%
e2722694
 
6.3%
22714217
 
6.3%
62711177
 
6.2%
Other values (6)15932538
36.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27219356
62.7%
Lowercase Letter16159908
37.3%

Most frequent character per category

ValueCountFrequency (%)
42795639
10.3%
32773388
10.2%
82751002
10.1%
02739763
10.1%
72732068
10.0%
22714217
10.0%
62711177
10.0%
92696143
9.9%
12656787
9.8%
52649172
9.7%
ValueCountFrequency (%)
c2762014
17.1%
a2744764
17.0%
e2722694
16.8%
d2704210
16.7%
b2655153
16.4%
f2571073
15.9%

Most occurring scripts

ValueCountFrequency (%)
Common27219356
62.7%
Latin16159908
37.3%

Most frequent character per script

ValueCountFrequency (%)
42795639
10.3%
32773388
10.2%
82751002
10.1%
02739763
10.1%
72732068
10.0%
22714217
10.0%
62711177
10.0%
92696143
9.9%
12656787
9.8%
52649172
9.7%
ValueCountFrequency (%)
c2762014
17.1%
a2744764
17.0%
e2722694
16.8%
d2704210
16.7%
b2655153
16.4%
f2571073
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
42795639
 
6.4%
32773388
 
6.4%
c2762014
 
6.4%
82751002
 
6.3%
a2744764
 
6.3%
02739763
 
6.3%
72732068
 
6.3%
e2722694
 
6.3%
22714217
 
6.3%
62711177
 
6.2%
Other values (6)15932538
36.7%

shipping_limit_date
Categorical

HIGH CARDINALITY

Distinct4446
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2018-02-28 11:48:12
 
6860
2017-12-21 02:30:41
 
6706
2017-12-04 22:31:28
 
5200
2018-02-16 18:10:32
 
4896
2017-12-11 15:13:44
 
4896
Other values (4441)
1327044 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters25756438
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017-04-27 16:50:18
2nd row2017-04-27 16:50:18
3rd row2017-04-27 16:50:18
4th row2017-04-27 16:50:18
5th row2017-04-27 16:50:18
ValueCountFrequency (%)
2018-02-28 11:48:126860
 
0.5%
2017-12-21 02:30:416706
 
0.5%
2017-12-04 22:31:285200
 
0.4%
2018-02-16 18:10:324896
 
0.4%
2017-12-11 15:13:444896
 
0.4%
2018-03-29 14:48:014896
 
0.4%
2017-11-21 16:30:474048
 
0.3%
2017-08-08 13:10:104004
 
0.3%
2018-06-14 15:30:524004
 
0.3%
2018-03-25 22:55:283942
 
0.3%
Other values (4436)1306150
96.4%
2021-03-16T17:57:48.647135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-3021712
 
0.8%
2018-02-2815961
 
0.6%
2017-12-2112833
 
0.5%
2017-12-0712456
 
0.5%
2018-05-1011284
 
0.4%
2018-03-2211192
 
0.4%
2017-12-1110920
 
0.4%
2017-12-0410520
 
0.4%
2018-03-2910158
 
0.4%
2018-03-0810122
 
0.4%
Other values (4546)2584046
95.3%

Most occurring characters

ValueCountFrequency (%)
04347492
16.9%
14173960
16.2%
23379482
13.1%
-2711204
10.5%
:2711204
10.5%
81417194
 
5.5%
1355602
 
5.3%
31298109
 
5.0%
51237121
 
4.8%
71137820
 
4.4%
Other values (3)1987250
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18978428
73.7%
Dash Punctuation2711204
 
10.5%
Other Punctuation2711204
 
10.5%
Space Separator1355602
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
04347492
22.9%
14173960
22.0%
23379482
17.8%
81417194
 
7.5%
31298109
 
6.8%
51237121
 
6.5%
71137820
 
6.0%
4919850
 
4.8%
6535390
 
2.8%
9532010
 
2.8%
ValueCountFrequency (%)
-2711204
100.0%
ValueCountFrequency (%)
1355602
100.0%
ValueCountFrequency (%)
:2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25756438
100.0%

Most frequent character per script

ValueCountFrequency (%)
04347492
16.9%
14173960
16.2%
23379482
13.1%
-2711204
10.5%
:2711204
10.5%
81417194
 
5.5%
1355602
 
5.3%
31298109
 
5.0%
51237121
 
4.8%
71137820
 
4.4%
Other values (3)1987250
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII25756438
100.0%

Most frequent character per block

ValueCountFrequency (%)
04347492
16.9%
14173960
16.2%
23379482
13.1%
-2711204
10.5%
:2711204
10.5%
81417194
 
5.5%
1355602
 
5.3%
31298109
 
5.0%
51237121
 
4.8%
71137820
 
4.4%
Other values (3)1987250
7.7%

price
Real number (ℝ≥0)

Distinct1264
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.9570513
Minimum3.85
Maximum2699
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:48.862560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.85
5-th percentile16.49
Q139.9
median69.98
Q3129.9
95-th percentile320
Maximum2699
Range2695.15
Interquartile range (IQR)90

Descriptive statistics

Standard deviation162.8143505
Coefficient of variation (CV)1.428734322
Kurtosis61.52966711
Mean113.9570513
Median Absolute Deviation (MAD)39.99
Skewness6.48077634
Sum154480406.7
Variance26508.51272
MonotocityNot monotonic
2021-03-16T17:57:49.101696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.935152
 
2.6%
59.934823
 
2.6%
49.925769
 
1.9%
99.920468
 
1.5%
29.9918096
 
1.3%
89.916706
 
1.2%
5916641
 
1.2%
29.915756
 
1.2%
119.913531
 
1.0%
3912462
 
0.9%
Other values (1254)1146198
84.6%
ValueCountFrequency (%)
3.85488
 
< 0.1%
4.22448
0.2%
5.35572
 
< 0.1%
5.9162
 
< 0.1%
6.121794
0.1%
ValueCountFrequency (%)
2699272
 
< 0.1%
235064
 
< 0.1%
2258128
 
< 0.1%
2110911
0.1%
1999.99202
 
< 0.1%

freight_value
Real number (ℝ≥0)

Distinct1651
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.9232295
Minimum0
Maximum306.06
Zeros1477
Zeros (%)0.1%
Memory size10.3 MiB
2021-03-16T17:57:49.306148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.78
Q112.48
median15.3
Q318.72
95-th percentile38.04
Maximum306.06
Range306.06
Interquartile range (IQR)6.24

Descriptive statistics

Standard deviation13.05048359
Coefficient of variation (CV)0.7281323711
Kurtosis75.89894667
Mean17.9232295
Median Absolute Deviation (MAD)3.19
Skewness6.160223598
Sum24296765.75
Variance170.3151219
MonotocityNot monotonic
2021-03-16T17:57:49.550493image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.7845834
 
3.4%
11.8534256
 
2.5%
15.130924
 
2.3%
14.126102
 
1.9%
18.2317828
 
1.3%
12.7914920
 
1.1%
16.1114080
 
1.0%
7.3912400
 
0.9%
8.7212223
 
0.9%
14.5211603
 
0.9%
Other values (1641)1135432
83.8%
ValueCountFrequency (%)
01477
0.1%
0.14218
 
< 0.1%
0.97206
 
< 0.1%
0.99146
 
< 0.1%
1.08168
 
< 0.1%
ValueCountFrequency (%)
306.06272
< 0.1%
224.3496
 
< 0.1%
187.43246
< 0.1%
154.9506
< 0.1%
149.0196
 
< 0.1%

product_category_name
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct66
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
cama_mesa_banho
152147 
esporte_lazer
115908 
beleza_saude
112514 
moveis_decoracao
103825 
informatica_acessorios
97410 
Other values (61)
773798 

Length

Max length46
Median length15
Mean length14.86230472
Min length3

Characters and Unicode

Total characters20147370
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowinformatica_acessorios
2nd rowinformatica_acessorios
3rd rowinformatica_acessorios
4th rowinformatica_acessorios
5th rowinformatica_acessorios
ValueCountFrequency (%)
cama_mesa_banho152147
 
11.2%
esporte_lazer115908
 
8.6%
beleza_saude112514
 
8.3%
moveis_decoracao103825
 
7.7%
informatica_acessorios97410
 
7.2%
utilidades_domesticas94041
 
6.9%
relogios_presentes65743
 
4.8%
ferramentas_jardim61651
 
4.5%
automotivo55582
 
4.1%
brinquedos51767
 
3.8%
Other values (56)445014
32.8%
2021-03-16T17:57:50.262589image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
cama_mesa_banho152147
 
11.2%
esporte_lazer115908
 
8.6%
beleza_saude112514
 
8.3%
moveis_decoracao103825
 
7.7%
informatica_acessorios97410
 
7.2%
utilidades_domesticas94041
 
6.9%
relogios_presentes65743
 
4.8%
ferramentas_jardim61651
 
4.5%
automotivo55582
 
4.1%
brinquedos51767
 
3.8%
Other values (56)445014
32.8%

Most occurring characters

ValueCountFrequency (%)
a2487458
12.3%
e2412183
12.0%
s1973460
9.8%
o1956003
9.7%
i1356677
 
6.7%
r1319133
 
6.5%
_1275345
 
6.3%
m970561
 
4.8%
t968320
 
4.8%
c938962
 
4.7%
Other values (18)4489268
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter18869891
93.7%
Connector Punctuation1275345
 
6.3%
Decimal Number2134
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a2487458
13.2%
e2412183
12.8%
s1973460
10.5%
o1956003
10.4%
i1356677
 
7.2%
r1319133
 
7.0%
m970561
 
5.1%
t968320
 
5.1%
c938962
 
5.0%
l670920
 
3.6%
Other values (16)3816214
20.2%
ValueCountFrequency (%)
_1275345
100.0%
ValueCountFrequency (%)
22134
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin18869891
93.7%
Common1277479
 
6.3%

Most frequent character per script

ValueCountFrequency (%)
a2487458
13.2%
e2412183
12.8%
s1973460
10.5%
o1956003
10.4%
i1356677
 
7.2%
r1319133
 
7.0%
m970561
 
5.1%
t968320
 
5.1%
c938962
 
5.0%
l670920
 
3.6%
Other values (16)3816214
20.2%
ValueCountFrequency (%)
_1275345
99.8%
22134
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII20147370
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2487458
12.3%
e2412183
12.0%
s1973460
9.8%
o1956003
9.7%
i1356677
 
6.7%
r1319133
 
6.5%
_1275345
 
6.3%
m970561
 
4.8%
t968320
 
4.8%
c938962
 
4.7%
Other values (18)4489268
22.3%

product_name_lenght
Real number (ℝ≥0)

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.50411478
Minimum5
Maximum72
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:50.475021image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q142
median51
Q357
95-th percentile60
Maximum72
Range67
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.997895677
Coefficient of variation (CV)0.2061246911
Kurtosis0.2785279607
Mean48.50411478
Median Absolute Deviation (MAD)7
Skewness-0.9087060628
Sum65752275
Variance99.95791796
MonotocityNot monotonic
2021-03-16T17:57:50.703411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6093800
 
6.9%
5989804
 
6.6%
5583562
 
6.2%
5678722
 
5.8%
5771135
 
5.2%
5865507
 
4.8%
5453895
 
4.0%
5349211
 
3.6%
5248213
 
3.6%
4646048
 
3.4%
Other values (46)675705
49.8%
ValueCountFrequency (%)
5479
 
< 0.1%
980
 
< 0.1%
11448
 
< 0.1%
121265
0.1%
14299
 
< 0.1%
ValueCountFrequency (%)
7289
 
< 0.1%
641965
 
0.1%
6314513
1.1%
621365
 
0.1%
612523
 
0.2%

product_description_lenght
Real number (ℝ≥0)

Distinct1506
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean782.471859
Minimum4
Maximum3976
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:50.915843image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile139
Q1340
median588
Q3963
95-th percentile2213
Maximum3976
Range3972
Interquartile range (IQR)623

Descriptive statistics

Standard deviation670.0575468
Coefficient of variation (CV)0.8563343705
Kurtosis4.220810738
Mean782.471859
Median Absolute Deviation (MAD)299
Skewness1.94172046
Sum1060720417
Variance448977.116
MonotocityNot monotonic
2021-03-16T17:57:51.131266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34811408
 
0.8%
34110070
 
0.7%
3669072
 
0.7%
2097606
 
0.6%
9197339
 
0.5%
3957062
 
0.5%
11977057
 
0.5%
556860
 
0.5%
2456013
 
0.4%
1895523
 
0.4%
Other values (1496)1277592
94.2%
ValueCountFrequency (%)
4273
< 0.1%
1547
 
< 0.1%
33184
 
< 0.1%
3675
 
< 0.1%
40563
< 0.1%
ValueCountFrequency (%)
3976426
< 0.1%
3963965
0.1%
3930184
 
< 0.1%
3896202
 
< 0.1%
389036
 
< 0.1%

product_photos_qty
Real number (ℝ≥0)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.127696772
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:51.352674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum14
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.659910411
Coefficient of variation (CV)0.7801442539
Kurtosis4.084264884
Mean2.127696772
Median Absolute Deviation (MAD)0
Skewness1.889847345
Sum2884310
Variance2.755302572
MonotocityNot monotonic
2021-03-16T17:57:51.563112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1715164
52.8%
2267740
 
19.8%
3140947
 
10.4%
494587
 
7.0%
558347
 
4.3%
643589
 
3.2%
719243
 
1.4%
87160
 
0.5%
94468
 
0.3%
102675
 
0.2%
Other values (4)1682
 
0.1%
ValueCountFrequency (%)
1715164
52.8%
2267740
 
19.8%
3140947
 
10.4%
494587
 
7.0%
558347
 
4.3%
ValueCountFrequency (%)
1484
 
< 0.1%
13490
 
< 0.1%
12531
 
< 0.1%
11577
 
< 0.1%
102675
0.2%

product_weight_g
Real number (ℝ≥0)

Distinct735
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2095.692298
Minimum2
Maximum30000
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:51.780650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile150
Q1300
median700
Q31800
95-th percentile10050
Maximum30000
Range29998
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3711.375285
Coefficient of variation (CV)1.770954299
Kurtosis13.22308861
Mean2095.692298
Median Absolute Deviation (MAD)500
Skewness3.335602984
Sum2840924671
Variance13774306.51
MonotocityNot monotonic
2021-03-16T17:57:52.026131image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20090430
 
6.7%
25058406
 
4.3%
15055611
 
4.1%
30051483
 
3.8%
40041719
 
3.1%
35038018
 
2.8%
10036604
 
2.7%
60036353
 
2.7%
50028153
 
2.1%
45023415
 
1.7%
Other values (725)895410
66.1%
ValueCountFrequency (%)
2626
 
< 0.1%
25352
 
< 0.1%
509593
0.7%
6734
 
< 0.1%
751735
 
0.1%
ValueCountFrequency (%)
300001159
0.1%
29050234
 
< 0.1%
28300240
 
< 0.1%
282501224
0.1%
2735083
 
< 0.1%

product_length_cm
Real number (ℝ≥0)

Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.65461692
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:52.266346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q119
median25
Q338
95-th percentile65
Maximum105
Range98
Interquartile range (IQR)19

Descriptive statistics

Standard deviation16.71502341
Coefficient of variation (CV)0.5452693621
Kurtosis3.904944394
Mean30.65461692
Median Absolute Deviation (MAD)8
Skewness1.820451353
Sum41555460
Variance279.3920076
MonotocityNot monotonic
2021-03-16T17:57:52.490748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16180776
 
13.3%
20138014
 
10.2%
3098975
 
7.3%
1767316
 
5.0%
1866330
 
4.9%
1963003
 
4.6%
2557693
 
4.3%
4054010
 
4.0%
2242834
 
3.2%
2133767
 
2.5%
Other values (81)552884
40.8%
ValueCountFrequency (%)
7691
 
0.1%
10520
 
< 0.1%
112439
0.2%
12488
 
< 0.1%
13462
 
< 0.1%
ValueCountFrequency (%)
1058239
0.6%
104716
 
0.1%
102471
 
< 0.1%
101888
 
0.1%
1004422
0.3%

product_height_cm
Real number (ℝ≥0)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.63488767
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:52.886688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile45
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.40669883
Coefficient of variation (CV)0.8059386452
Kurtosis7.032585721
Mean16.63488767
Median Absolute Deviation (MAD)6
Skewness2.212346076
Sum22550287
Variance179.7395736
MonotocityNot monotonic
2021-03-16T17:57:53.094132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10123993
 
9.1%
1195930
 
7.1%
1586260
 
6.4%
2072006
 
5.3%
1265033
 
4.8%
763667
 
4.7%
557462
 
4.2%
256373
 
4.2%
853365
 
3.9%
1651362
 
3.8%
Other values (71)630151
46.5%
ValueCountFrequency (%)
256373
4.2%
336212
2.7%
446790
3.5%
557462
4.2%
634108
2.5%
ValueCountFrequency (%)
1051169
0.1%
10473
 
< 0.1%
103684
0.1%
1001040
0.1%
95742
0.1%

product_width_cm
Real number (ℝ≥0)

Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.41726333
Minimum9
Maximum118
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:53.322523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range109
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.36974918
Coefficient of variation (CV)0.5282320572
Kurtosis5.934172395
Mean23.41726333
Median Absolute Deviation (MAD)7
Skewness1.925422166
Sum31744489
Variance153.0106948
MonotocityNot monotonic
2021-03-16T17:57:53.538943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20125728
 
9.3%
11112363
 
8.3%
15104546
 
7.7%
30102793
 
7.6%
1699944
 
7.4%
1380271
 
5.9%
1269186
 
5.1%
4059450
 
4.4%
1457988
 
4.3%
2552388
 
3.9%
Other values (61)490945
36.2%
ValueCountFrequency (%)
91287
 
0.1%
101097
 
0.1%
11112363
8.3%
1269186
5.1%
1380271
5.9%
ValueCountFrequency (%)
118162
 
< 0.1%
100123
 
< 0.1%
926362
0.5%
841172
 
0.1%
83384
 
< 0.1%

seller_id
Categorical

HIGH CARDINALITY

Distinct362
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
6c99c983ce3b6ba0ab813e6790e81691
 
96500
6b3bd31ad8fcda4b2635ec9f3ff2ecdf
 
60214
4125d9385a25e82d2f72d3a0fd55bc3f
 
45505
52a50b42accf164f9f019941e5759d9b
 
39520
9cf787a239c1aa29dbd76f153dc13f9a
 
28908
Other values (357)
1084955 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row33cbbec1e7e1044aaf11d152172c776f
2nd row33cbbec1e7e1044aaf11d152172c776f
3rd row33cbbec1e7e1044aaf11d152172c776f
4th row33cbbec1e7e1044aaf11d152172c776f
5th row33cbbec1e7e1044aaf11d152172c776f
ValueCountFrequency (%)
6c99c983ce3b6ba0ab813e6790e8169196500
 
7.1%
6b3bd31ad8fcda4b2635ec9f3ff2ecdf60214
 
4.4%
4125d9385a25e82d2f72d3a0fd55bc3f45505
 
3.4%
52a50b42accf164f9f019941e5759d9b39520
 
2.9%
9cf787a239c1aa29dbd76f153dc13f9a28908
 
2.1%
516e7738bd8f735ac19a010ee5450d8d26460
 
2.0%
9c1c0c36cd23c20897e473901a8fb14925110
 
1.9%
5f5a58930c3c35f3b5af264f34fb8c8522848
 
1.7%
165b1235e9e9942cb5fae67103576fb022848
 
1.7%
c32fc744b9160ac853450488e3cfea9322848
 
1.7%
Other values (352)964841
71.2%
2021-03-16T17:57:54.037282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
6c99c983ce3b6ba0ab813e6790e8169196500
 
7.1%
6b3bd31ad8fcda4b2635ec9f3ff2ecdf60214
 
4.4%
4125d9385a25e82d2f72d3a0fd55bc3f45505
 
3.4%
52a50b42accf164f9f019941e5759d9b39520
 
2.9%
9cf787a239c1aa29dbd76f153dc13f9a28908
 
2.1%
516e7738bd8f735ac19a010ee5450d8d26460
 
2.0%
9c1c0c36cd23c20897e473901a8fb14925110
 
1.9%
5f5a58930c3c35f3b5af264f34fb8c8522848
 
1.7%
165b1235e9e9942cb5fae67103576fb022848
 
1.7%
c32fc744b9160ac853450488e3cfea9322848
 
1.7%
Other values (352)964841
71.2%

Most occurring characters

ValueCountFrequency (%)
33112454
 
7.2%
c3041290
 
7.0%
93037903
 
7.0%
53021523
 
7.0%
12899959
 
6.7%
82792847
 
6.4%
f2753940
 
6.3%
b2708779
 
6.2%
a2638666
 
6.1%
02580002
 
5.9%
Other values (6)14791901
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27257723
62.8%
Lowercase Letter16121541
37.2%

Most frequent character per category

ValueCountFrequency (%)
33112454
11.4%
93037903
11.1%
53021523
11.1%
12899959
10.6%
82792847
10.2%
02580002
9.5%
22545006
9.3%
62531479
9.3%
42385846
8.8%
72350704
8.6%
ValueCountFrequency (%)
c3041290
18.9%
f2753940
17.1%
b2708779
16.8%
a2638666
16.4%
e2538186
15.7%
d2440680
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common27257723
62.8%
Latin16121541
37.2%

Most frequent character per script

ValueCountFrequency (%)
33112454
11.4%
93037903
11.1%
53021523
11.1%
12899959
10.6%
82792847
10.2%
02580002
9.5%
22545006
9.3%
62531479
9.3%
42385846
8.8%
72350704
8.6%
ValueCountFrequency (%)
c3041290
18.9%
f2753940
17.1%
b2708779
16.8%
a2638666
16.4%
e2538186
15.7%
d2440680
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
33112454
 
7.2%
c3041290
 
7.0%
93037903
 
7.0%
53021523
 
7.0%
12899959
 
6.7%
82792847
 
6.4%
f2753940
 
6.3%
b2708779
 
6.2%
a2638666
 
6.1%
02580002
 
5.9%
Other values (6)14791901
34.1%

seller_zip_code_prefix
Real number (ℝ≥0)

HIGH CORRELATION

Distinct346
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27640.01326
Minimum1022
Maximum99670
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:54.235738image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1022
5-th percentile7091
Q112306
median14940
Q337410
95-th percentile89295
Maximum99670
Range98648
Interquartile range (IQR)25104

Descriptive statistics

Standard deviation25358.88909
Coefficient of variation (CV)0.9174702216
Kurtosis1.612837899
Mean27640.01326
Median Absolute Deviation (MAD)5899
Skewness1.685516931
Sum3.746885726 × 1010
Variance643073256.1
MonotocityNot monotonic
2021-03-16T17:57:54.442173image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3840096500
 
7.1%
1402068544
 
5.1%
2277560214
 
4.4%
1494048048
 
3.5%
1106545505
 
3.4%
3042142532
 
3.1%
1382039520
 
2.9%
1333028910
 
2.1%
1440328908
 
2.1%
9370028724
 
2.1%
Other values (336)868197
64.0%
ValueCountFrequency (%)
102217
 
< 0.1%
102320
 
< 0.1%
1026156
< 0.1%
1031124
< 0.1%
104150
 
< 0.1%
ValueCountFrequency (%)
99670429
 
< 0.1%
995006710
0.5%
987801668
 
0.1%
970505397
0.4%
95840806
 
0.1%

seller_city
Categorical

HIGH CARDINALITY

Distinct163
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
rio de janeiro
151539 
uberlandia
108657 
ribeirao preto
90341 
sao paulo
 
80172
santos
 
64527
Other values (158)
860366 

Length

Max length26
Median length10
Mean length11.12402018
Min length3

Characters and Unicode

Total characters15079744
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbento goncalves
2nd rowbento goncalves
3rd rowbento goncalves
4th rowbento goncalves
5th rowbento goncalves
ValueCountFrequency (%)
rio de janeiro151539
 
11.2%
uberlandia108657
 
8.0%
ribeirao preto90341
 
6.7%
sao paulo80172
 
5.9%
santos64527
 
4.8%
belo horizonte56282
 
4.2%
praia grande55019
 
4.1%
ibitinga48048
 
3.5%
santo andre39646
 
2.9%
monte alegre do sul39520
 
2.9%
Other values (153)621851
45.9%
2021-03-16T17:57:54.903509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rio174491
 
6.9%
de157036
 
6.2%
janeiro151539
 
6.0%
sao148909
 
5.9%
do118473
 
4.7%
uberlandia108657
 
4.3%
preto100501
 
4.0%
ribeirao91360
 
3.6%
paulo82736
 
3.3%
sul75399
 
3.0%
Other values (187)1327250
52.3%

Most occurring characters

ValueCountFrequency (%)
a2245405
14.9%
o1638329
10.9%
r1545623
10.2%
i1328956
8.8%
e1226809
 
8.1%
1180749
 
7.8%
n834046
 
5.5%
s649007
 
4.3%
d638444
 
4.2%
t576758
 
3.8%
Other values (16)3215618
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13891733
92.1%
Space Separator1180749
 
7.8%
Other Punctuation6732
 
< 0.1%
Dash Punctuation530
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a2245405
16.2%
o1638329
11.8%
r1545623
11.1%
i1328956
9.6%
e1226809
8.8%
n834046
 
6.0%
s649007
 
4.7%
d638444
 
4.6%
t576758
 
4.2%
b501830
 
3.6%
Other values (13)2706526
19.5%
ValueCountFrequency (%)
1180749
100.0%
ValueCountFrequency (%)
-530
100.0%
ValueCountFrequency (%)
/6732
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13891733
92.1%
Common1188011
 
7.9%

Most frequent character per script

ValueCountFrequency (%)
a2245405
16.2%
o1638329
11.8%
r1545623
11.1%
i1328956
9.6%
e1226809
8.8%
n834046
 
6.0%
s649007
 
4.7%
d638444
 
4.6%
t576758
 
4.2%
b501830
 
3.6%
Other values (13)2706526
19.5%
ValueCountFrequency (%)
1180749
99.4%
/6732
 
0.6%
-530
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15079744
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2245405
14.9%
o1638329
10.9%
r1545623
10.2%
i1328956
8.8%
e1226809
 
8.1%
1180749
 
7.8%
n834046
 
5.5%
s649007
 
4.3%
d638444
 
4.2%
t576758
 
3.8%
Other values (16)3215618
21.3%

seller_state
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
SP
760960 
MG
228955 
RJ
180581 
RS
 
67266
PR
 
58708
Other values (8)
 
59132

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2711204
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRS
2nd rowRS
3rd rowRS
4th rowRS
5th rowRS
ValueCountFrequency (%)
SP760960
56.1%
MG228955
 
16.9%
RJ180581
 
13.3%
RS67266
 
5.0%
PR58708
 
4.3%
SC35775
 
2.6%
GO9813
 
0.7%
BA5961
 
0.4%
DF2677
 
0.2%
ES2615
 
0.2%
Other values (3)2291
 
0.2%
2021-03-16T17:57:55.347405image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp760960
56.1%
mg228955
 
16.9%
rj180581
 
13.3%
rs67266
 
5.0%
pr58708
 
4.3%
sc35775
 
2.6%
go9813
 
0.7%
ba5961
 
0.4%
df2677
 
0.2%
es2615
 
0.2%
Other values (3)2291
 
0.2%

Most occurring characters

ValueCountFrequency (%)
S866616
32.0%
P821696
30.3%
R306555
 
11.3%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2711204
100.0%

Most frequent character per category

ValueCountFrequency (%)
S866616
32.0%
P821696
30.3%
R306555
 
11.3%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin2711204
100.0%

Most frequent character per script

ValueCountFrequency (%)
S866616
32.0%
P821696
30.3%
R306555
 
11.3%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2711204
100.0%

Most frequent character per block

ValueCountFrequency (%)
S866616
32.0%
P821696
30.3%
R306555
 
11.3%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

geolocation_zip_code_prefix
Real number (ℝ≥0)

HIGH CORRELATION

Distinct346
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27640.01326
Minimum1022
Maximum99670
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:55.557888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1022
5-th percentile7091
Q112306
median14940
Q337410
95-th percentile89295
Maximum99670
Range98648
Interquartile range (IQR)25104

Descriptive statistics

Standard deviation25358.88909
Coefficient of variation (CV)0.9174702216
Kurtosis1.612837899
Mean27640.01326
Median Absolute Deviation (MAD)5899
Skewness1.685516931
Sum3.746885726 × 1010
Variance643073256.1
MonotocityNot monotonic
2021-03-16T17:57:55.975456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3840096500
 
7.1%
1402068544
 
5.1%
2277560214
 
4.4%
1494048048
 
3.5%
1106545505
 
3.4%
3042142532
 
3.1%
1382039520
 
2.9%
1333028910
 
2.1%
1440328908
 
2.1%
9370028724
 
2.1%
Other values (336)868197
64.0%
ValueCountFrequency (%)
102217
 
< 0.1%
102320
 
< 0.1%
1026156
< 0.1%
1031124
< 0.1%
104150
 
< 0.1%
ValueCountFrequency (%)
99670429
 
< 0.1%
995006710
0.5%
987801668
 
0.1%
970505397
0.4%
95840806
 
0.1%

geolocation_lat
Real number (ℝ)

Distinct30805
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-22.69291567
Minimum-36.60537441
Maximum-3.779843191
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:56.189887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-36.60537441
5-th percentile-27.78121345
Q1-23.65353826
median-22.90294032
Q3-21.20790147
95-th percentile-18.91404141
Maximum-3.779843191
Range32.82553122
Interquartile range (IQR)2.445636783

Descriptive statistics

Standard deviation2.552272944
Coefficient of variation (CV)-0.1124700316
Kurtosis3.863297755
Mean-22.69291567
Median Absolute Deviation (MAD)1.062608387
Skewness-0.005075975693
Sum-30762561.87
Variance6.51409718
MonotocityNot monotonic
2021-03-16T17:57:56.419548image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-22.9659055610591
 
0.8%
-23.004582253204
 
0.2%
-22.972424362737
 
0.2%
-22.675045521748
 
0.1%
-22.968224721547
 
0.1%
-22.968219011547
 
0.1%
-22.992687421428
 
0.1%
-22.972954721428
 
0.1%
-23.968458611425
 
0.1%
-19.942525591372
 
0.1%
Other values (30795)1328575
98.0%
ValueCountFrequency (%)
-36.6053744112
 
< 0.1%
-36.6038367924
< 0.1%
-31.4188647812
 
< 0.1%
-30.1342295620
< 0.1%
-30.1204723540
< 0.1%
ValueCountFrequency (%)
-3.7798431913
< 0.1%
-3.7807851373
< 0.1%
-3.7810661383
< 0.1%
-3.7814962733
< 0.1%
-3.7818617853
< 0.1%

geolocation_lng
Real number (ℝ)

Distinct30813
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-46.92442715
Minimum-64.28743301
Maximum-34.84768694
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:56.669649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-64.28743301
5-th percentile-51.08402284
Q1-48.2755815
median-46.89585858
Q3-46.32862351
95-th percentile-43.28496162
Maximum-34.84768694
Range29.43974607
Interquartile range (IQR)1.946957993

Descriptive statistics

Standard deviation2.345820217
Coefficient of variation (CV)-0.04999145135
Kurtosis1.577338835
Mean-46.92442715
Median Absolute Deviation (MAD)1.286562712
Skewness0.224370835
Sum-63610847.29
Variance5.502872491
MonotocityNot monotonic
2021-03-16T17:57:56.902070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-43.389998710591
 
0.8%
-43.319899323204
 
0.2%
-43.359506672737
 
0.2%
-46.977239241748
 
0.1%
-43.386518331547
 
0.1%
-43.394779471547
 
0.1%
-43.392388481428
 
0.1%
-43.365460171428
 
0.1%
-46.353776151425
 
0.1%
-43.97550851372
 
0.1%
Other values (30803)1328575
98.0%
ValueCountFrequency (%)
-64.2874330124
< 0.1%
-64.2839464612
< 0.1%
-60.3362652512
< 0.1%
-58.1285617712
< 0.1%
-54.6716816612
< 0.1%
ValueCountFrequency (%)
-34.8476869411
< 0.1%
-34.847765511
< 0.1%
-34.8478561822
< 0.1%
-34.8495256811
< 0.1%
-34.850079811
< 0.1%

geolocation_city
Categorical

HIGH CARDINALITY

Distinct223
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
rio de janeiro
151539 
uberlandia
 
89571
ribeirao preto
 
78560
santos
 
64527
belo horizonte
 
60972
Other values (218)
910433 

Length

Max length21
Median length10
Mean length11.15801688
Min length3

Characters and Unicode

Total characters15125830
Distinct characters36
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbento goncalves
2nd rowbento goncalves
3rd rowbento goncalves
4th rowbento goncalves
5th rowbento goncalves
ValueCountFrequency (%)
rio de janeiro151539
 
11.2%
uberlandia89571
 
6.6%
ribeirao preto78560
 
5.8%
santos64527
 
4.8%
belo horizonte60972
 
4.5%
praia grande55019
 
4.1%
sao paulo51558
 
3.8%
ibitinga48048
 
3.5%
santo andre39449
 
2.9%
jaguariuna35662
 
2.6%
Other values (213)680697
50.2%
2021-03-16T17:57:57.415645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rio174491
 
7.1%
de157036
 
6.4%
janeiro151539
 
6.1%
sao123725
 
5.0%
preto103525
 
4.2%
do100310
 
4.1%
uberlandia89571
 
3.6%
ribeirao78763
 
3.2%
santos64527
 
2.6%
belo60972
 
2.5%
Other values (236)1361410
55.2%

Most occurring characters

ValueCountFrequency (%)
a2283543
15.1%
o1597031
10.6%
r1590989
10.5%
i1380163
9.1%
e1177226
 
7.8%
1110267
 
7.3%
n873332
 
5.8%
d633728
 
4.2%
s610957
 
4.0%
t565919
 
3.7%
Other values (26)3302675
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14012463
92.6%
Space Separator1110267
 
7.3%
Dash Punctuation1768
 
< 0.1%
Other Punctuation1332
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a2283543
16.3%
o1597031
11.4%
r1590989
11.4%
i1380163
9.8%
e1177226
8.4%
n873332
 
6.2%
d633728
 
4.5%
s610957
 
4.4%
t565919
 
4.0%
u522264
 
3.7%
Other values (23)2777311
19.8%
ValueCountFrequency (%)
1110267
100.0%
ValueCountFrequency (%)
'1332
100.0%
ValueCountFrequency (%)
-1768
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14012463
92.6%
Common1113367
 
7.4%

Most frequent character per script

ValueCountFrequency (%)
a2283543
16.3%
o1597031
11.4%
r1590989
11.4%
i1380163
9.8%
e1177226
8.4%
n873332
 
6.2%
d633728
 
4.5%
s610957
 
4.4%
t565919
 
4.0%
u522264
 
3.7%
Other values (23)2777311
19.8%
ValueCountFrequency (%)
1110267
99.7%
-1768
 
0.2%
'1332
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15025924
99.3%
None99906
 
0.7%

Most frequent character per block

ValueCountFrequency (%)
a2283543
15.2%
o1597031
10.6%
r1590989
10.6%
i1380163
9.2%
e1177226
 
7.8%
1110267
 
7.4%
n873332
 
5.8%
d633728
 
4.2%
s610957
 
4.1%
t565919
 
3.8%
Other values (16)3202769
21.3%
ValueCountFrequency (%)
ã40186
40.2%
â20057
20.1%
é10228
 
10.2%
í7091
 
7.1%
ç6920
 
6.9%
á5314
 
5.3%
ó3172
 
3.2%
ú2504
 
2.5%
ê2343
 
2.3%
õ2091
 
2.1%

geolocation_state
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
SP
759198 
MG
228955 
RJ
180581 
RS
 
67266
PR
 
60470
Other values (8)
 
59132

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2711204
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRS
2nd rowRS
3rd rowRS
4th rowRS
5th rowRS
ValueCountFrequency (%)
SP759198
56.0%
MG228955
 
16.9%
RJ180581
 
13.3%
RS67266
 
5.0%
PR60470
 
4.5%
SC35775
 
2.6%
GO9813
 
0.7%
BA5961
 
0.4%
DF2677
 
0.2%
ES2615
 
0.2%
Other values (3)2291
 
0.2%
2021-03-16T17:57:57.889341image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp759198
56.0%
mg228955
 
16.9%
rj180581
 
13.3%
rs67266
 
5.0%
pr60470
 
4.5%
sc35775
 
2.6%
go9813
 
0.7%
ba5961
 
0.4%
df2677
 
0.2%
es2615
 
0.2%
Other values (3)2291
 
0.2%

Most occurring characters

ValueCountFrequency (%)
S864854
31.9%
P821696
30.3%
R308317
 
11.4%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2711204
100.0%

Most frequent character per category

ValueCountFrequency (%)
S864854
31.9%
P821696
30.3%
R308317
 
11.4%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin2711204
100.0%

Most frequent character per script

ValueCountFrequency (%)
S864854
31.9%
P821696
30.3%
R308317
 
11.4%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2711204
100.0%

Most frequent character per block

ValueCountFrequency (%)
S864854
31.9%
P821696
30.3%
R308317
 
11.4%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

customer_unique_id
Categorical

HIGH CARDINALITY

Distinct4322
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
c0536ee7a009264e0f1bf1c8f4c54ad3
 
7344
f7ea4eef770a388bd5b225acfc546604
 
6860
31e412b9fb766b6794724ed17a41dfa6
 
6706
37d2d1ac62901f9a36cff32ca86c9337
 
5790
38a4f1deb45ca914dd13c73b41775d71
 
5200
Other values (4317)
1323702 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0299fedc3f190307e87f32483a9ba4fd
2nd row0299fedc3f190307e87f32483a9ba4fd
3rd row0299fedc3f190307e87f32483a9ba4fd
4th row0299fedc3f190307e87f32483a9ba4fd
5th row0299fedc3f190307e87f32483a9ba4fd
ValueCountFrequency (%)
c0536ee7a009264e0f1bf1c8f4c54ad37344
 
0.5%
f7ea4eef770a388bd5b225acfc5466046860
 
0.5%
31e412b9fb766b6794724ed17a41dfa66706
 
0.5%
37d2d1ac62901f9a36cff32ca86c93375790
 
0.4%
38a4f1deb45ca914dd13c73b41775d715200
 
0.4%
ed04da2a9e4d7cb3eae84074a28225674896
 
0.4%
ae977f6cf6981d883940f7e618620b3e4896
 
0.4%
c01ac50f437f46cc8fffb6d1a01d86314340
 
0.3%
6469f99c1f9dfae7733b25662e7f17824311
 
0.3%
6d394722d5fc5e721aee6875a218d8db4048
 
0.3%
Other values (4312)1301211
96.0%
2021-03-16T17:57:58.365529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
c0536ee7a009264e0f1bf1c8f4c54ad37344
 
0.5%
f7ea4eef770a388bd5b225acfc5466046860
 
0.5%
31e412b9fb766b6794724ed17a41dfa66706
 
0.5%
37d2d1ac62901f9a36cff32ca86c93375790
 
0.4%
38a4f1deb45ca914dd13c73b41775d715200
 
0.4%
ed04da2a9e4d7cb3eae84074a28225674896
 
0.4%
ae977f6cf6981d883940f7e618620b3e4896
 
0.4%
c01ac50f437f46cc8fffb6d1a01d86314340
 
0.3%
6469f99c1f9dfae7733b25662e7f17824311
 
0.3%
6d394722d5fc5e721aee6875a218d8db4048
 
0.3%
Other values (4312)1301211
96.0%

Most occurring characters

ValueCountFrequency (%)
62799933
 
6.5%
f2772626
 
6.4%
c2768305
 
6.4%
a2765491
 
6.4%
22752555
 
6.3%
12733068
 
6.3%
52732417
 
6.3%
72729395
 
6.3%
d2703601
 
6.2%
b2698264
 
6.2%
Other values (6)15923609
36.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number26982060
62.2%
Lowercase Letter16397204
37.8%

Most frequent character per category

ValueCountFrequency (%)
62799933
10.4%
22752555
10.2%
12733068
10.1%
52732417
10.1%
72729395
10.1%
42696492
10.0%
02659215
9.9%
32652971
9.8%
82621919
9.7%
92604095
9.7%
ValueCountFrequency (%)
f2772626
16.9%
c2768305
16.9%
a2765491
16.9%
d2703601
16.5%
b2698264
16.5%
e2688917
16.4%

Most occurring scripts

ValueCountFrequency (%)
Common26982060
62.2%
Latin16397204
37.8%

Most frequent character per script

ValueCountFrequency (%)
62799933
10.4%
22752555
10.2%
12733068
10.1%
52732417
10.1%
72729395
10.1%
42696492
10.0%
02659215
9.9%
32652971
9.8%
82621919
9.7%
92604095
9.7%
ValueCountFrequency (%)
f2772626
16.9%
c2768305
16.9%
a2765491
16.9%
d2703601
16.5%
b2698264
16.5%
e2688917
16.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
62799933
 
6.5%
f2772626
 
6.4%
c2768305
 
6.4%
a2765491
 
6.4%
22752555
 
6.3%
12733068
 
6.3%
52732417
 
6.3%
72729395
 
6.3%
d2703601
 
6.2%
b2698264
 
6.2%
Other values (6)15923609
36.7%

customer_zip_code_prefix
Real number (ℝ≥0)

HIGH CORRELATION

Distinct346
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27640.01326
Minimum1022
Maximum99670
Zeros0
Zeros (%)0.0%
Memory size10.3 MiB
2021-03-16T17:57:58.572974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1022
5-th percentile7091
Q112306
median14940
Q337410
95-th percentile89295
Maximum99670
Range98648
Interquartile range (IQR)25104

Descriptive statistics

Standard deviation25358.88909
Coefficient of variation (CV)0.9174702216
Kurtosis1.612837899
Mean27640.01326
Median Absolute Deviation (MAD)5899
Skewness1.685516931
Sum3.746885726 × 1010
Variance643073256.1
MonotocityNot monotonic
2021-03-16T17:57:58.787401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3840096500
 
7.1%
1402068544
 
5.1%
2277560214
 
4.4%
1494048048
 
3.5%
1106545505
 
3.4%
3042142532
 
3.1%
1382039520
 
2.9%
1333028910
 
2.1%
1440328908
 
2.1%
9370028724
 
2.1%
Other values (336)868197
64.0%
ValueCountFrequency (%)
102217
 
< 0.1%
102320
 
< 0.1%
1026156
< 0.1%
1031124
< 0.1%
104150
 
< 0.1%
ValueCountFrequency (%)
99670429
 
< 0.1%
995006710
0.5%
987801668
 
0.1%
970505397
0.4%
95840806
 
0.1%

customer_city
Categorical

HIGH CARDINALITY

Distinct155
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
rio de janeiro
151539 
uberlandia
108657 
ribeirao preto
94125 
santos
 
64527
belo horizonte
 
60972
Other values (150)
875782 

Length

Max length21
Median length10
Mean length11.15866088
Min length3

Characters and Unicode

Total characters15126703
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbento goncalves
2nd rowbento goncalves
3rd rowbento goncalves
4th rowbento goncalves
5th rowbento goncalves
ValueCountFrequency (%)
rio de janeiro151539
 
11.2%
uberlandia108657
 
8.0%
ribeirao preto94125
 
6.9%
santos64527
 
4.8%
belo horizonte60972
 
4.5%
sao paulo60660
 
4.5%
praia grande55019
 
4.1%
ibitinga48048
 
3.5%
santo andre47272
 
3.5%
jaguariuna37998
 
2.8%
Other values (145)626785
46.2%
2021-03-16T17:57:59.438739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
rio174491
 
7.1%
de157036
 
6.4%
janeiro151539
 
6.1%
sao146481
 
5.9%
uberlandia108657
 
4.4%
preto103525
 
4.2%
do100161
 
4.1%
ribeirao94384
 
3.8%
santos64527
 
2.6%
horizonte60972
 
2.5%
Other values (179)1302638
52.9%

Most occurring characters

ValueCountFrequency (%)
a2349969
15.5%
o1601901
10.6%
r1591243
10.5%
i1387415
9.2%
e1189475
 
7.9%
1108809
 
7.3%
n873322
 
5.8%
d633814
 
4.2%
s611317
 
4.0%
t566034
 
3.7%
Other values (16)3213404
21.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14013314
92.6%
Space Separator1108809
 
7.3%
Dash Punctuation3068
 
< 0.1%
Other Punctuation1512
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
a2349969
16.8%
o1601901
11.4%
r1591243
11.4%
i1387415
9.9%
e1189475
8.5%
n873322
 
6.2%
d633814
 
4.5%
s611317
 
4.4%
t566034
 
4.0%
u524968
 
3.7%
Other values (13)2683856
19.2%
ValueCountFrequency (%)
1108809
100.0%
ValueCountFrequency (%)
'1512
100.0%
ValueCountFrequency (%)
-3068
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14013314
92.6%
Common1113389
 
7.4%

Most frequent character per script

ValueCountFrequency (%)
a2349969
16.8%
o1601901
11.4%
r1591243
11.4%
i1387415
9.9%
e1189475
8.5%
n873322
 
6.2%
d633814
 
4.5%
s611317
 
4.4%
t566034
 
4.0%
u524968
 
3.7%
Other values (13)2683856
19.2%
ValueCountFrequency (%)
1108809
99.6%
-3068
 
0.3%
'1512
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII15126703
100.0%

Most frequent character per block

ValueCountFrequency (%)
a2349969
15.5%
o1601901
10.6%
r1591243
10.5%
i1387415
9.2%
e1189475
 
7.9%
1108809
 
7.3%
n873322
 
5.8%
d633814
 
4.2%
s611317
 
4.0%
t566034
 
3.7%
Other values (16)3213404
21.2%

customer_state
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
SP
759198 
MG
228955 
RJ
180581 
RS
 
67266
PR
 
60470
Other values (8)
 
59132

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2711204
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRS
2nd rowRS
3rd rowRS
4th rowRS
5th rowRS
ValueCountFrequency (%)
SP759198
56.0%
MG228955
 
16.9%
RJ180581
 
13.3%
RS67266
 
5.0%
PR60470
 
4.5%
SC35775
 
2.6%
GO9813
 
0.7%
BA5961
 
0.4%
DF2677
 
0.2%
ES2615
 
0.2%
Other values (3)2291
 
0.2%
2021-03-16T17:57:59.884637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp759198
56.0%
mg228955
 
16.9%
rj180581
 
13.3%
rs67266
 
5.0%
pr60470
 
4.5%
sc35775
 
2.6%
go9813
 
0.7%
ba5961
 
0.4%
df2677
 
0.2%
es2615
 
0.2%
Other values (3)2291
 
0.2%

Most occurring characters

ValueCountFrequency (%)
S864854
31.9%
P821696
30.3%
R308317
 
11.4%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2711204
100.0%

Most frequent character per category

ValueCountFrequency (%)
S864854
31.9%
P821696
30.3%
R308317
 
11.4%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin2711204
100.0%

Most frequent character per script

ValueCountFrequency (%)
S864854
31.9%
P821696
30.3%
R308317
 
11.4%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2711204
100.0%

Most frequent character per block

ValueCountFrequency (%)
S864854
31.9%
P821696
30.3%
R308317
 
11.4%
G238768
 
8.8%
M228955
 
8.4%
J180581
 
6.7%
C36038
 
1.3%
O9813
 
0.4%
B6357
 
0.2%
A5961
 
0.2%
Other values (3)9864
 
0.4%

mql_id
Categorical

HIGH CARDINALITY

Distinct362
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
973f72ab89f64e22470778a9bd1ea10f
 
96500
a0ab09eb2842e474a3a5aed12e533a2a
 
60214
d6e0ddfc5b05597877a7d5036789a3a1
 
45505
085df4d5a370218b679cfab2121cd194
 
39520
42d15f0a37b7d0a151bb2ca9cc7e5548
 
28908
Other values (357)
1084955 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1ce04284ef15be769958c668292573b8
2nd row1ce04284ef15be769958c668292573b8
3rd row1ce04284ef15be769958c668292573b8
4th row1ce04284ef15be769958c668292573b8
5th row1ce04284ef15be769958c668292573b8
ValueCountFrequency (%)
973f72ab89f64e22470778a9bd1ea10f96500
 
7.1%
a0ab09eb2842e474a3a5aed12e533a2a60214
 
4.4%
d6e0ddfc5b05597877a7d5036789a3a145505
 
3.4%
085df4d5a370218b679cfab2121cd19439520
 
2.9%
42d15f0a37b7d0a151bb2ca9cc7e554828908
 
2.1%
e0bcbf50b9afa71ee130b94dfe8bcf2e26460
 
2.0%
7290c4082936311a3295b7c9ca7bda8f25110
 
1.9%
66744053f818d4032f5ba881340db02022848
 
1.7%
a58f8a3280b2dd6440d88af9decb9b1f22848
 
1.7%
3469f647707d7517364600b07bd4581622848
 
1.7%
Other values (352)964841
71.2%
2021-03-16T17:58:00.340533image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
973f72ab89f64e22470778a9bd1ea10f96500
 
7.1%
a0ab09eb2842e474a3a5aed12e533a2a60214
 
4.4%
d6e0ddfc5b05597877a7d5036789a3a145505
 
3.4%
085df4d5a370218b679cfab2121cd19439520
 
2.9%
42d15f0a37b7d0a151bb2ca9cc7e554828908
 
2.1%
e0bcbf50b9afa71ee130b94dfe8bcf2e26460
 
2.0%
7290c4082936311a3295b7c9ca7bda8f25110
 
1.9%
66744053f818d4032f5ba881340db02022848
 
1.7%
a58f8a3280b2dd6440d88af9decb9b1f22848
 
1.7%
3469f647707d7517364600b07bd4581622848
 
1.7%
Other values (352)964841
71.2%

Most occurring characters

ValueCountFrequency (%)
73133291
 
7.2%
a3090580
 
7.1%
22865346
 
6.6%
82825141
 
6.5%
12798133
 
6.5%
e2797516
 
6.4%
02792522
 
6.4%
42777646
 
6.4%
b2759333
 
6.4%
f2679634
 
6.2%
Other values (6)14860122
34.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number27232075
62.8%
Lowercase Letter16147189
37.2%

Most frequent character per category

ValueCountFrequency (%)
73133291
11.5%
22865346
10.5%
82825141
10.4%
12798133
10.3%
02792522
10.3%
42777646
10.2%
52646894
9.7%
92622569
9.6%
32542385
9.3%
62228148
8.2%
ValueCountFrequency (%)
a3090580
19.1%
e2797516
17.3%
b2759333
17.1%
f2679634
16.6%
d2590115
16.0%
c2230011
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common27232075
62.8%
Latin16147189
37.2%

Most frequent character per script

ValueCountFrequency (%)
73133291
11.5%
22865346
10.5%
82825141
10.4%
12798133
10.3%
02792522
10.3%
42777646
10.2%
52646894
9.7%
92622569
9.6%
32542385
9.3%
62228148
8.2%
ValueCountFrequency (%)
a3090580
19.1%
e2797516
17.3%
b2759333
17.1%
f2679634
16.6%
d2590115
16.0%
c2230011
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
73133291
 
7.2%
a3090580
 
7.1%
22865346
 
6.6%
82825141
 
6.5%
12798133
 
6.5%
e2797516
 
6.4%
02792522
 
6.4%
42777646
 
6.4%
b2759333
 
6.4%
f2679634
 
6.2%
Other values (6)14860122
34.3%

sdr_id
Categorical

HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
4b339f9567d060bcea4f5136b9f5949e
236790 
068066e24f0c643eb1d089c7dd20cd73
216836 
a8387c01a09e99ce014107505b92388c
139492 
de63de0d10a6012430098db33c679b0b
123186 
9e4d1098a3b0f5da39b0bc48f9876645
119939 
Other values (13)
519359 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowde63de0d10a6012430098db33c679b0b
2nd rowde63de0d10a6012430098db33c679b0b
3rd rowde63de0d10a6012430098db33c679b0b
4th rowde63de0d10a6012430098db33c679b0b
5th rowde63de0d10a6012430098db33c679b0b
ValueCountFrequency (%)
4b339f9567d060bcea4f5136b9f5949e236790
17.5%
068066e24f0c643eb1d089c7dd20cd73216836
16.0%
a8387c01a09e99ce014107505b92388c139492
10.3%
de63de0d10a6012430098db33c679b0b123186
9.1%
9e4d1098a3b0f5da39b0bc48f9876645119939
8.8%
9d12ef1a7eca3ec58c545c678af7869c84778
 
6.3%
56bf83c4bb35763a51c2baab501b4c6776345
 
5.6%
370c9f455f93a9a96cbe9bea48e7003373764
 
5.4%
b90f87164b5f8c2cfa5c8572834dbe3f62057
 
4.6%
0a0fb2b07d841f84fb6714e35c72307560508
 
4.5%
Other values (8)161907
11.9%
2021-03-16T17:58:00.777432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4b339f9567d060bcea4f5136b9f5949e236790
17.5%
068066e24f0c643eb1d089c7dd20cd73216836
16.0%
a8387c01a09e99ce014107505b92388c139492
10.3%
de63de0d10a6012430098db33c679b0b123186
9.1%
9e4d1098a3b0f5da39b0bc48f9876645119939
8.8%
9d12ef1a7eca3ec58c545c678af7869c84778
 
6.3%
56bf83c4bb35763a51c2baab501b4c6776345
 
5.6%
370c9f455f93a9a96cbe9bea48e7003373764
 
5.4%
b90f87164b5f8c2cfa5c8572834dbe3f62057
 
4.6%
0a0fb2b07d841f84fb6714e35c72307560508
 
4.5%
Other values (8)161907
11.9%

Most occurring characters

ValueCountFrequency (%)
04463983
 
10.3%
93697399
 
8.5%
33198551
 
7.4%
b3152187
 
7.3%
63092574
 
7.1%
c2823108
 
6.5%
42749016
 
6.3%
52632019
 
6.1%
82504734
 
5.8%
d2428297
 
5.6%
Other values (6)12637396
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number28401668
65.5%
Lowercase Letter14977596
34.5%

Most frequent character per category

ValueCountFrequency (%)
04463983
15.7%
93697399
13.0%
33198551
11.3%
63092574
10.9%
42749016
9.7%
52632019
9.3%
82504734
8.8%
72333875
8.2%
12161898
7.6%
21567619
 
5.5%
ValueCountFrequency (%)
b3152187
21.0%
c2823108
18.8%
d2428297
16.2%
e2370297
15.8%
f2306075
15.4%
a1897632
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common28401668
65.5%
Latin14977596
34.5%

Most frequent character per script

ValueCountFrequency (%)
04463983
15.7%
93697399
13.0%
33198551
11.3%
63092574
10.9%
42749016
9.7%
52632019
9.3%
82504734
8.8%
72333875
8.2%
12161898
7.6%
21567619
 
5.5%
ValueCountFrequency (%)
b3152187
21.0%
c2823108
18.8%
d2428297
16.2%
e2370297
15.8%
f2306075
15.4%
a1897632
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
04463983
 
10.3%
93697399
 
8.5%
33198551
 
7.4%
b3152187
 
7.3%
63092574
 
7.1%
c2823108
 
6.5%
42749016
 
6.3%
52632019
 
6.1%
82504734
 
5.8%
d2428297
 
5.6%
Other values (6)12637396
29.1%

sr_id
Categorical

HIGH CORRELATION

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
d3d1e91a157ea7f90548eef82f1955e3
282247 
4ef15afb4b2723d8f3d81e51ec7afefe
264075 
495d4e95a8cf8bbf8b432b612a2aa328
134044 
fbf4aef3f6915dc0c3c97d6812522f6a
127357 
6565aa9ce3178a5caf6171827af3a9ba
116736 
Other values (13)
431143 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6565aa9ce3178a5caf6171827af3a9ba
2nd row6565aa9ce3178a5caf6171827af3a9ba
3rd row6565aa9ce3178a5caf6171827af3a9ba
4th row6565aa9ce3178a5caf6171827af3a9ba
5th row6565aa9ce3178a5caf6171827af3a9ba
ValueCountFrequency (%)
d3d1e91a157ea7f90548eef82f1955e3282247
20.8%
4ef15afb4b2723d8f3d81e51ec7afefe264075
19.5%
495d4e95a8cf8bbf8b432b612a2aa328134044
9.9%
fbf4aef3f6915dc0c3c97d6812522f6a127357
9.4%
6565aa9ce3178a5caf6171827af3a9ba116736
8.6%
85fc447d336637ba1df43e793199fbc876734
 
5.7%
a8387c01a09e99ce014107505b92388c75509
 
5.6%
9e4d1098a3b0f5da39b0bc48f987664567632
 
5.0%
2695de1affa7750089c0455f8ce2702142056
 
3.1%
9ae085775a198122c5586fa830ff7f2b40216
 
3.0%
Other values (8)128996
9.5%
2021-03-16T17:58:01.173342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d3d1e91a157ea7f90548eef82f1955e3282247
20.8%
4ef15afb4b2723d8f3d81e51ec7afefe264075
19.5%
495d4e95a8cf8bbf8b432b612a2aa328134044
9.9%
fbf4aef3f6915dc0c3c97d6812522f6a127357
9.4%
6565aa9ce3178a5caf6171827af3a9ba116736
8.6%
85fc447d336637ba1df43e793199fbc876734
 
5.7%
a8387c01a09e99ce014107505b92388c75509
 
5.6%
9e4d1098a3b0f5da39b0bc48f987664567632
 
5.0%
2695de1affa7750089c0455f8ce2702142056
 
3.1%
9ae085775a198122c5586fa830ff7f2b40216
 
3.0%
Other values (8)128996
9.5%

Most occurring characters

ValueCountFrequency (%)
f4117504
 
9.5%
e3706573
 
8.5%
13529533
 
8.1%
a3470629
 
8.0%
53380770
 
7.8%
83108466
 
7.2%
32843894
 
6.6%
92819360
 
6.5%
72453784
 
5.7%
22341790
 
5.4%
Other values (6)11606961
26.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number25981359
59.9%
Lowercase Letter17397905
40.1%

Most frequent character per category

ValueCountFrequency (%)
13529533
13.6%
53380770
13.0%
83108466
12.0%
32843894
10.9%
92819360
10.9%
72453784
9.4%
22341790
9.0%
42127336
8.2%
01708284
6.6%
61668142
6.4%
ValueCountFrequency (%)
f4117504
23.7%
e3706573
21.3%
a3470629
19.9%
b2117966
12.2%
d2116386
12.2%
c1868847
10.7%

Most occurring scripts

ValueCountFrequency (%)
Common25981359
59.9%
Latin17397905
40.1%

Most frequent character per script

ValueCountFrequency (%)
13529533
13.6%
53380770
13.0%
83108466
12.0%
32843894
10.9%
92819360
10.9%
72453784
9.4%
22341790
9.0%
42127336
8.2%
01708284
6.6%
61668142
6.4%
ValueCountFrequency (%)
f4117504
23.7%
e3706573
21.3%
a3470629
19.9%
b2117966
12.2%
d2116386
12.2%
c1868847
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
f4117504
 
9.5%
e3706573
 
8.5%
13529533
 
8.1%
a3470629
 
8.0%
53380770
 
7.8%
83108466
 
7.2%
32843894
 
6.6%
92819360
 
6.5%
72453784
 
5.7%
22341790
 
5.4%
Other values (6)11606961
26.8%

won_date
Categorical

HIGH CARDINALITY

Distinct354
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2018-04-16 18:25:12
 
96500
2018-03-13 18:53:47
 
60214
2018-05-04 03:00:00
 
59027
2018-04-05 15:06:11
 
45505
2018-01-24 12:27:42
 
28908
Other values (349)
1065448 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters25756438
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-03-06 13:27:32
2nd row2018-03-06 13:27:32
3rd row2018-03-06 13:27:32
4th row2018-03-06 13:27:32
5th row2018-03-06 13:27:32
ValueCountFrequency (%)
2018-04-16 18:25:1296500
 
7.1%
2018-03-13 18:53:4760214
 
4.4%
2018-05-04 03:00:0059027
 
4.4%
2018-04-05 15:06:1145505
 
3.4%
2018-01-24 12:27:4228908
 
2.1%
2018-05-08 13:32:3426460
 
2.0%
2018-03-19 20:26:2725110
 
1.9%
2018-04-30 16:01:3522848
 
1.7%
2018-02-26 17:07:0622848
 
1.7%
2018-03-05 20:54:0322848
 
1.7%
Other values (344)945334
69.7%
2021-03-16T17:58:01.596211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-04-16102648
 
3.8%
18:25:1296500
 
3.6%
03:00:0089504
 
3.3%
2018-03-1374342
 
2.7%
18:53:4760214
 
2.2%
2018-05-0459027
 
2.2%
2018-04-0556276
 
2.1%
15:06:1145505
 
1.7%
2018-02-2644350
 
1.6%
2018-05-0843123
 
1.6%
Other values (436)2039715
75.2%

Most occurring characters

ValueCountFrequency (%)
04580380
17.8%
13863691
15.0%
23240327
12.6%
-2711204
10.5%
:2711204
10.5%
81980712
7.7%
1355602
 
5.3%
41303849
 
5.1%
31195546
 
4.6%
51152867
 
4.5%
Other values (3)1661056
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number18978428
73.7%
Dash Punctuation2711204
 
10.5%
Other Punctuation2711204
 
10.5%
Space Separator1355602
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
04580380
24.1%
13863691
20.4%
23240327
17.1%
81980712
10.4%
41303849
 
6.9%
31195546
 
6.3%
51152867
 
6.1%
6687455
 
3.6%
7587283
 
3.1%
9386318
 
2.0%
ValueCountFrequency (%)
-2711204
100.0%
ValueCountFrequency (%)
1355602
100.0%
ValueCountFrequency (%)
:2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25756438
100.0%

Most frequent character per script

ValueCountFrequency (%)
04580380
17.8%
13863691
15.0%
23240327
12.6%
-2711204
10.5%
:2711204
10.5%
81980712
7.7%
1355602
 
5.3%
41303849
 
5.1%
31195546
 
4.6%
51152867
 
4.5%
Other values (3)1661056
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII25756438
100.0%

Most frequent character per block

ValueCountFrequency (%)
04580380
17.8%
13863691
15.0%
23240327
12.6%
-2711204
10.5%
:2711204
10.5%
81980712
7.7%
1355602
 
5.3%
41303849
 
5.1%
31195546
 
4.6%
51152867
 
4.5%
Other values (3)1661056
 
6.4%

business_segment
Categorical

HIGH CORRELATION

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
home_decor
202484 
health_beauty
121177 
toys
110649 
car_accessories
108835 
construction_tools_house_garden
108198 
Other values (24)
704259 

Length

Max length31
Median length13
Mean length13.8465796
Min length3

Characters and Unicode

Total characters18770451
Distinct characters23
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousehold_utilities
2nd rowhousehold_utilities
3rd rowhousehold_utilities
4th rowhousehold_utilities
5th rowhousehold_utilities
ValueCountFrequency (%)
home_decor202484
14.9%
health_beauty121177
 
8.9%
toys110649
 
8.2%
car_accessories108835
 
8.0%
construction_tools_house_garden108198
 
8.0%
food_supplement97811
 
7.2%
food_drink86545
 
6.4%
household_utilities84483
 
6.2%
audio_video_electronics75458
 
5.6%
pet42638
 
3.1%
Other values (19)317324
23.4%
2021-03-16T17:58:02.008110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
home_decor202484
14.9%
health_beauty121177
 
8.9%
toys110649
 
8.2%
car_accessories108835
 
8.0%
construction_tools_house_garden108198
 
8.0%
food_supplement97811
 
7.2%
food_drink86545
 
6.4%
household_utilities84483
 
6.2%
audio_video_electronics75458
 
5.6%
pet42638
 
3.1%
Other values (19)317324
23.4%

Most occurring characters

ValueCountFrequency (%)
o2201219
11.7%
e2065706
 
11.0%
s1547969
 
8.2%
_1422957
 
7.6%
t1275095
 
6.8%
c1146515
 
6.1%
i1062773
 
5.7%
a1010966
 
5.4%
r993894
 
5.3%
d873867
 
4.7%
Other values (13)5169490
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter17347494
92.4%
Connector Punctuation1422957
 
7.6%

Most frequent character per category

ValueCountFrequency (%)
o2201219
12.7%
e2065706
11.9%
s1547969
 
8.9%
t1275095
 
7.4%
c1146515
 
6.6%
i1062773
 
6.1%
a1010966
 
5.8%
r993894
 
5.7%
d873867
 
5.0%
h833389
 
4.8%
Other values (12)4336101
25.0%
ValueCountFrequency (%)
_1422957
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin17347494
92.4%
Common1422957
 
7.6%

Most frequent character per script

ValueCountFrequency (%)
o2201219
12.7%
e2065706
11.9%
s1547969
 
8.9%
t1275095
 
7.4%
c1146515
 
6.6%
i1062773
 
6.1%
a1010966
 
5.8%
r993894
 
5.7%
d873867
 
5.0%
h833389
 
4.8%
Other values (12)4336101
25.0%
ValueCountFrequency (%)
_1422957
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18770451
100.0%

Most frequent character per block

ValueCountFrequency (%)
o2201219
11.7%
e2065706
 
11.0%
s1547969
 
8.2%
_1422957
 
7.6%
t1275095
 
6.8%
c1146515
 
6.1%
i1062773
 
5.7%
a1010966
 
5.4%
r993894
 
5.3%
d873867
 
4.7%
Other values (13)5169490
27.5%

lead_type
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
online_medium
598266 
online_small
275414 
online_big
260802 
industry
99146 
offline
65726 
Other values (2)
 
56248

Length

Max length15
Median length12
Mean length11.60306712
Min length7

Characters and Unicode

Total characters15729141
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowonline_big
2nd rowonline_big
3rd rowonline_big
4th rowonline_big
5th rowonline_big
ValueCountFrequency (%)
online_medium598266
44.1%
online_small275414
20.3%
online_big260802
19.2%
industry99146
 
7.3%
offline65726
 
4.8%
online_beginner44593
 
3.3%
online_top11655
 
0.9%
2021-03-16T17:58:02.414373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-16T17:58:02.594892image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
online_medium598266
44.1%
online_small275414
20.3%
online_big260802
19.2%
industry99146
 
7.3%
offline65726
 
4.8%
online_beginner44593
 
3.3%
online_top11655
 
0.9%

Most occurring characters

ValueCountFrequency (%)
n2635518
16.8%
i2259263
14.4%
e1943908
12.4%
l1807284
11.5%
m1471946
9.4%
o1268111
8.1%
_1190730
7.6%
d697412
 
4.4%
u697412
 
4.4%
s374560
 
2.4%
Other values (8)1382997
8.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14538411
92.4%
Connector Punctuation1190730
 
7.6%

Most frequent character per category

ValueCountFrequency (%)
n2635518
18.1%
i2259263
15.5%
e1943908
13.4%
l1807284
12.4%
m1471946
10.1%
o1268111
8.7%
d697412
 
4.8%
u697412
 
4.8%
s374560
 
2.6%
b305395
 
2.1%
Other values (7)1077602
7.4%
ValueCountFrequency (%)
_1190730
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14538411
92.4%
Common1190730
 
7.6%

Most frequent character per script

ValueCountFrequency (%)
n2635518
18.1%
i2259263
15.5%
e1943908
13.4%
l1807284
12.4%
m1471946
10.1%
o1268111
8.7%
d697412
 
4.8%
u697412
 
4.8%
s374560
 
2.6%
b305395
 
2.1%
Other values (7)1077602
7.4%
ValueCountFrequency (%)
_1190730
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15729141
100.0%

Most frequent character per block

ValueCountFrequency (%)
n2635518
16.8%
i2259263
14.4%
e1943908
12.4%
l1807284
11.5%
m1471946
9.4%
o1268111
8.1%
_1190730
7.6%
d697412
 
4.4%
u697412
 
4.4%
s374560
 
2.4%
Other values (8)1382997
8.8%

lead_behaviour_profile
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
cat
997828 
wolf
154936 
eagle
147775 
shark
 
29128
cat, wolf
 
21266

Length

Max length10
Median length3
Mean length3.493523173
Min length3

Characters and Unicode

Total characters4735827
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcat
2nd rowcat
3rd rowcat
4th rowcat
5th rowcat
ValueCountFrequency (%)
cat997828
73.6%
wolf154936
 
11.4%
eagle147775
 
10.9%
shark29128
 
2.1%
cat, wolf21266
 
1.6%
eagle, cat4669
 
0.3%
2021-03-16T17:58:03.167633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-16T17:58:03.343163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
cat1023763
74.1%
wolf176202
 
12.8%
eagle152444
 
11.0%
shark29128
 
2.1%

Most occurring characters

ValueCountFrequency (%)
a1205335
25.5%
c1023763
21.6%
t1023763
21.6%
l328646
 
6.9%
e304888
 
6.4%
w176202
 
3.7%
o176202
 
3.7%
f176202
 
3.7%
g152444
 
3.2%
s29128
 
0.6%
Other values (5)139254
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter4683957
98.9%
Other Punctuation25935
 
0.5%
Space Separator25935
 
0.5%

Most frequent character per category

ValueCountFrequency (%)
a1205335
25.7%
c1023763
21.9%
t1023763
21.9%
l328646
 
7.0%
e304888
 
6.5%
w176202
 
3.8%
o176202
 
3.8%
f176202
 
3.8%
g152444
 
3.3%
s29128
 
0.6%
Other values (3)87384
 
1.9%
ValueCountFrequency (%)
,25935
100.0%
ValueCountFrequency (%)
25935
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4683957
98.9%
Common51870
 
1.1%

Most frequent character per script

ValueCountFrequency (%)
a1205335
25.7%
c1023763
21.9%
t1023763
21.9%
l328646
 
7.0%
e304888
 
6.5%
w176202
 
3.8%
o176202
 
3.8%
f176202
 
3.8%
g152444
 
3.3%
s29128
 
0.6%
Other values (3)87384
 
1.9%
ValueCountFrequency (%)
,25935
50.0%
25935
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII4735827
100.0%

Most frequent character per block

ValueCountFrequency (%)
a1205335
25.5%
c1023763
21.6%
t1023763
21.6%
l328646
 
6.9%
e304888
 
6.4%
w176202
 
3.7%
o176202
 
3.7%
f176202
 
3.7%
g152444
 
3.2%
s29128
 
0.6%
Other values (5)139254
 
2.9%

business_type
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
reseller
1038366 
manufacturer
317236 

Length

Max length12
Median length8
Mean length8.936074157
Min length8

Characters and Unicode

Total characters12113760
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreseller
2nd rowreseller
3rd rowreseller
4th rowreseller
5th rowreseller
ValueCountFrequency (%)
reseller1038366
76.6%
manufacturer317236
 
23.4%
2021-03-16T17:58:03.744092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-16T17:58:03.896684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
reseller1038366
76.6%
manufacturer317236
 
23.4%

Most occurring characters

ValueCountFrequency (%)
e3432334
28.3%
r2711204
22.4%
l2076732
17.1%
s1038366
 
8.6%
a634472
 
5.2%
u634472
 
5.2%
m317236
 
2.6%
n317236
 
2.6%
f317236
 
2.6%
c317236
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter12113760
100.0%

Most frequent character per category

ValueCountFrequency (%)
e3432334
28.3%
r2711204
22.4%
l2076732
17.1%
s1038366
 
8.6%
a634472
 
5.2%
u634472
 
5.2%
m317236
 
2.6%
n317236
 
2.6%
f317236
 
2.6%
c317236
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Latin12113760
100.0%

Most frequent character per script

ValueCountFrequency (%)
e3432334
28.3%
r2711204
22.4%
l2076732
17.1%
s1038366
 
8.6%
a634472
 
5.2%
u634472
 
5.2%
m317236
 
2.6%
n317236
 
2.6%
f317236
 
2.6%
c317236
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII12113760
100.0%

Most frequent character per block

ValueCountFrequency (%)
e3432334
28.3%
r2711204
22.4%
l2076732
17.1%
s1038366
 
8.6%
a634472
 
5.2%
u634472
 
5.2%
m317236
 
2.6%
n317236
 
2.6%
f317236
 
2.6%
c317236
 
2.6%

declared_monthly_revenue
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
0.0
1355602 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4066806
Distinct characters2
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0
ValueCountFrequency (%)
0.01355602
100.0%
2021-03-16T17:58:04.272693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-16T17:58:04.444923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.01355602
100.0%

Most occurring characters

ValueCountFrequency (%)
02711204
66.7%
.1355602
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2711204
66.7%
Other Punctuation1355602
33.3%

Most frequent character per category

ValueCountFrequency (%)
02711204
100.0%
ValueCountFrequency (%)
.1355602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4066806
100.0%

Most frequent character per script

ValueCountFrequency (%)
02711204
66.7%
.1355602
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII4066806
100.0%

Most frequent character per block

ValueCountFrequency (%)
02711204
66.7%
.1355602
33.3%

first_contact_date
Categorical

HIGH CARDINALITY

Distinct141
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
2018-04-11
106718 
2018-01-22
 
65230
2018-01-10
 
49716
2018-03-30
 
46030
2018-05-09
 
44027
Other values (136)
1043881 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters13556020
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-02-03
2nd row2018-02-03
3rd row2018-02-03
4th row2018-02-03
5th row2018-02-03
ValueCountFrequency (%)
2018-04-11106718
 
7.9%
2018-01-2265230
 
4.8%
2018-01-1049716
 
3.7%
2018-03-3046030
 
3.4%
2018-05-0944027
 
3.2%
2018-04-2139520
 
2.9%
2018-05-0238976
 
2.9%
2018-02-1037214
 
2.7%
2018-04-0333937
 
2.5%
2018-02-2132989
 
2.4%
Other values (131)861245
63.5%
2021-03-16T17:58:04.817279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-04-11106718
 
7.9%
2018-01-2265230
 
4.8%
2018-01-1049716
 
3.7%
2018-03-3046030
 
3.4%
2018-05-0944027
 
3.2%
2018-04-2139520
 
2.9%
2018-05-0238976
 
2.9%
2018-02-1037214
 
2.7%
2018-04-0333937
 
2.5%
2018-02-2132989
 
2.4%
Other values (131)861245
63.5%

Most occurring characters

ValueCountFrequency (%)
03260363
24.1%
-2711204
20.0%
12404479
17.7%
22227512
16.4%
81404509
10.4%
4476126
 
3.5%
3357079
 
2.6%
5287993
 
2.1%
7191856
 
1.4%
6126272
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number10844816
80.0%
Dash Punctuation2711204
 
20.0%

Most frequent character per category

ValueCountFrequency (%)
03260363
30.1%
12404479
22.2%
22227512
20.5%
81404509
13.0%
4476126
 
4.4%
3357079
 
3.3%
5287993
 
2.7%
7191856
 
1.8%
6126272
 
1.2%
9108627
 
1.0%
ValueCountFrequency (%)
-2711204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13556020
100.0%

Most frequent character per script

ValueCountFrequency (%)
03260363
24.1%
-2711204
20.0%
12404479
17.7%
22227512
16.4%
81404509
10.4%
4476126
 
3.5%
3357079
 
2.6%
5287993
 
2.1%
7191856
 
1.4%
6126272
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII13556020
100.0%

Most frequent character per block

ValueCountFrequency (%)
03260363
24.1%
-2711204
20.0%
12404479
17.7%
22227512
16.4%
81404509
10.4%
4476126
 
3.5%
3357079
 
2.6%
5287993
 
2.1%
7191856
 
1.4%
6126272
 
0.9%

landing_page_id
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct70
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
22c29808c4f815213303f8933030604c
312272 
b76ef37428e6799c421989521c0e5077
205242 
4e82dd1f6d00626bda0723eef0a269a6
96500 
ce1a65abd0973638f1c887a6efcfa82d
92959 
40dec9f3d5259a3d2dbcdab2114fae47
78576 
Other values (65)
570053 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters43379264
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb76ef37428e6799c421989521c0e5077
2nd rowb76ef37428e6799c421989521c0e5077
3rd rowb76ef37428e6799c421989521c0e5077
4th rowb76ef37428e6799c421989521c0e5077
5th rowb76ef37428e6799c421989521c0e5077
ValueCountFrequency (%)
22c29808c4f815213303f8933030604c312272
23.0%
b76ef37428e6799c421989521c0e5077205242
15.1%
4e82dd1f6d00626bda0723eef0a269a696500
 
7.1%
ce1a65abd0973638f1c887a6efcfa82d92959
 
6.9%
40dec9f3d5259a3d2dbcdab2114fae4778576
 
5.8%
0d6bc3c00e4e64927cae2e8d9c6a0b9b60214
 
4.4%
f017be4dbf86243af5c1ebed0cff36a245938
 
3.4%
358adb2fee9a122549618e46925a00a537562
 
2.8%
a0fbc0263cb98bdb0d72b9f9f283a17237336
 
2.8%
a7982125ff7aa3b2054c6e44f9d2852232155
 
2.4%
Other values (60)356848
26.3%
2021-03-16T17:58:05.316900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
22c29808c4f815213303f8933030604c312272
23.0%
b76ef37428e6799c421989521c0e5077205242
15.1%
4e82dd1f6d00626bda0723eef0a269a696500
 
7.1%
ce1a65abd0973638f1c887a6efcfa82d92959
 
6.9%
40dec9f3d5259a3d2dbcdab2114fae4778576
 
5.8%
0d6bc3c00e4e64927cae2e8d9c6a0b9b60214
 
4.4%
f017be4dbf86243af5c1ebed0cff36a245938
 
3.4%
358adb2fee9a122549618e46925a00a537562
 
2.8%
a0fbc0263cb98bdb0d72b9f9f283a17237336
 
2.8%
a7982125ff7aa3b2054c6e44f9d2852232155
 
2.4%
Other values (60)356848
26.3%

Most occurring characters

ValueCountFrequency (%)
03957871
 
9.1%
23941763
 
9.1%
33493220
 
8.1%
83329358
 
7.7%
92981093
 
6.9%
c2851449
 
6.6%
f2584124
 
6.0%
62569170
 
5.9%
e2546185
 
5.9%
12493796
 
5.7%
Other values (6)12631235
29.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number29639299
68.3%
Lowercase Letter13739965
31.7%

Most frequent character per category

ValueCountFrequency (%)
03957871
13.4%
23941763
13.3%
33493220
11.8%
83329358
11.2%
92981093
10.1%
62569170
8.7%
12493796
8.4%
42479432
8.4%
72327693
7.9%
52065903
7.0%
ValueCountFrequency (%)
c2851449
20.8%
f2584124
18.8%
e2546185
18.5%
a2017595
14.7%
d1977494
14.4%
b1763118
12.8%

Most occurring scripts

ValueCountFrequency (%)
Common29639299
68.3%
Latin13739965
31.7%

Most frequent character per script

ValueCountFrequency (%)
03957871
13.4%
23941763
13.3%
33493220
11.8%
83329358
11.2%
92981093
10.1%
62569170
8.7%
12493796
8.4%
42479432
8.4%
72327693
7.9%
52065903
7.0%
ValueCountFrequency (%)
c2851449
20.8%
f2584124
18.8%
e2546185
18.5%
a2017595
14.7%
d1977494
14.4%
b1763118
12.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII43379264
100.0%

Most frequent character per block

ValueCountFrequency (%)
03957871
 
9.1%
23941763
 
9.1%
33493220
 
8.1%
83329358
 
7.7%
92981093
 
6.9%
c2851449
 
6.6%
f2584124
 
6.0%
62569170
 
5.9%
e2546185
 
5.9%
12493796
 
5.7%
Other values (6)12631235
29.1%

origin
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.3 MiB
organic_search
411205 
paid_search
379567 
unknown
239726 
direct_traffic
194693 
social
57226 
Other values (4)
73185 

Length

Max length14
Median length11
Mean length11.2248064
Min length5

Characters and Unicode

Total characters15216370
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowunknown
2nd rowunknown
3rd rowunknown
4th rowunknown
5th rowunknown
ValueCountFrequency (%)
organic_search411205
30.3%
paid_search379567
28.0%
unknown239726
17.7%
direct_traffic194693
14.4%
social57226
 
4.2%
referral39656
 
2.9%
display26115
 
1.9%
email4346
 
0.3%
other3068
 
0.2%
2021-03-16T17:58:05.789350image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
2021-03-16T17:58:05.961928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
organic_search411205
30.3%
paid_search379567
28.0%
unknown239726
17.7%
direct_traffic194693
14.4%
social57226
 
4.2%
referral39656
 
2.9%
display26115
 
1.9%
email4346
 
0.3%
other3068
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a1903580
12.5%
r1713399
11.3%
c1648589
10.8%
i1267845
8.3%
n1130383
 
7.4%
e1072191
 
7.0%
_985465
 
6.5%
s874113
 
5.7%
h793840
 
5.2%
o711225
 
4.7%
Other values (11)3115740
20.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter14230905
93.5%
Connector Punctuation985465
 
6.5%

Most frequent character per category

ValueCountFrequency (%)
a1903580
13.4%
r1713399
12.0%
c1648589
11.6%
i1267845
8.9%
n1130383
7.9%
e1072191
7.5%
s874113
 
6.1%
h793840
 
5.6%
o711225
 
5.0%
d600375
 
4.2%
Other values (10)2515365
17.7%
ValueCountFrequency (%)
_985465
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14230905
93.5%
Common985465
 
6.5%

Most frequent character per script

ValueCountFrequency (%)
a1903580
13.4%
r1713399
12.0%
c1648589
11.6%
i1267845
8.9%
n1130383
7.9%
e1072191
7.5%
s874113
 
6.1%
h793840
 
5.6%
o711225
 
5.0%
d600375
 
4.2%
Other values (10)2515365
17.7%
ValueCountFrequency (%)
_985465
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII15216370
100.0%

Most frequent character per block

ValueCountFrequency (%)
a1903580
12.5%
r1713399
11.3%
c1648589
10.8%
i1267845
8.3%
n1130383
 
7.4%
e1072191
 
7.0%
_985465
 
6.5%
s874113
 
5.7%
h793840
 
5.2%
o711225
 
4.7%
Other values (11)3115740
20.5%

Interactions

2021-03-16T17:47:47.759941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:47:48.932501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:47:50.271958image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:47:51.683457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:47:53.200414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:47:54.602327image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:47:55.825027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:47:57.193222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:47:58.357510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:47:59.726887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:00.956635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:02.242783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:03.481913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:04.734280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:06.069643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:07.451783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:08.617735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:09.942223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:11.319939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:12.616422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:13.861235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:14.909266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:16.016301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:17.235048image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:18.518611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:19.946832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:21.192490image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:22.237666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:23.326755image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:24.658195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:25.876935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:27.110637image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:28.323394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:29.471326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:30.684117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:32.154943image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:33.036587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:34.258320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:36.051671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:36.997994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:37.935487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:39.104394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:39.966058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:41.015251image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:41.685497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:42.478676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:43.418165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:44.692722image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:46.320370image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:48:48.021170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:53:48.739853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:53:53.355272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:53:57.167081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:53:57.964946image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:53:58.699980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:53:59.343261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:00.017458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:00.655751image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:01.404749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:02.095900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:02.706035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:03.329213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:03.970498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:04.563897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:05.106882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:05.649159image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:06.186145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:06.733430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:07.333065image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:07.894387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:08.503115image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:09.429630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:10.310276image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:11.129086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:12.016489image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:12.598286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:13.215635image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:13.818710image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:14.417471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:14.974324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:15.508193image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:16.064239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:16.633626image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:17.185740image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:17.750231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:18.296940image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:18.868412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:19.437978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:20.009412image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:20.578451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:21.175575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:21.733897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:22.291469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:22.862132image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:23.405204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:23.978633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:24.563864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:25.322836image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:25.917523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:26.595641image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:27.194971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:27.740823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:28.361415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:28.997956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:29.657058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:30.224052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:30.803997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:31.379169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:31.953730image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:32.486347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:33.041357image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:33.651801image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:34.185671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:34.743263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:35.307529image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:35.918624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:36.478282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:37.084629image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:37.697988image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:38.300323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:38.877286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:39.487995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:40.108371image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:40.695019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:41.390239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:41.990939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:42.601475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:43.126469image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:43.699424image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:44.267373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:44.993432image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:45.747416image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:46.350925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:46.951208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:47.563644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:48.145090image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:48.737545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:49.313270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:49.900888image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:50.482301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:51.071809image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:51.652067image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:52.246437image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:52.833898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:53.422388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:53.963187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:54.528602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:55.098165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:55.652378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:56.336818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:56.903399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:57.494822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:58.125582image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:58.746459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:59.373095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:54:59.946017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:00.512899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:01.104828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:01.694150image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:02.268053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:02.846864image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:03.427752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:04.020900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:04.533345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:05.073709image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:05.625562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:06.144216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:06.705117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:07.256459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:07.820756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:08.406194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:08.976264image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:09.562200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:10.279069image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:10.863453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:11.447835image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:12.020563image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:12.581476image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:13.156204image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:13.743630image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:14.309122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:14.833146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:15.381644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:15.951278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:16.478646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:17.076190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:17.647654image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:18.225955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:18.798254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:19.354823image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:19.936909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:20.529213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:21.114951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:21.707375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:22.389514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:23.095506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:23.809524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:24.494094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:25.340863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:25.991369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:26.655348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:27.318991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:27.991781image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:28.694141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:29.383299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:30.111552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:30.824689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:31.544807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:32.233018image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:32.927127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:33.622306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:34.306890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:34.989098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:35.665296image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:36.350467image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:37.040484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:37.715137image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:38.337388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:39.003488image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:39.658735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:40.303012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:40.981198image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:41.646452image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:42.349540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:43.293763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:43.961968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:44.706122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:45.358238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:46.025456image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:46.683687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:47.334922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:47.964272image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:48.648409image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:49.325636image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:50.045675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:50.621768image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:51.230523image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:51.903319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:52.590482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:53.288615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:53.888055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:54.555026image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:55.268552image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:55.863965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:56.484274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:57.171728image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:57.847959image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:58.535559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:55:59.175850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:00.042157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:00.693415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:01.415486image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:02.118643image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:02.787887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:03.465110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:04.174453image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:04.845497image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:05.466965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:06.138782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:06.899344image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:07.597470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:08.286645image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:09.093556image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:09.880890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:10.702092image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:11.600651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:12.388583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:13.108619image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:13.825703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:14.517109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:15.229042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:15.878450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:16.568791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:17.282804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:18.109532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:18.880431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:19.649444image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:20.436270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:21.202512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:21.968247image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:22.749172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:23.444707image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:24.151141image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:24.830522image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:25.525624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:26.224825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:26.926024image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:27.684031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:28.392254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:29.056482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:29.748373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:30.445509image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:31.093811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:32.071163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:32.752428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:33.442796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:34.124829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:34.794039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:35.465229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:36.329118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:36.936492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:37.615646image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:38.320799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:39.035849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:39.742000image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:40.441185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:41.147311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:41.802526image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:42.466749image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:43.160950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:43.854129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:44.548383image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:45.239734image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:45.927893image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:46.648077image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:47.316047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:47.991460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:48.660668image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:49.320508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:50.004096image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:50.730160image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:51.406352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:52.100496image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:52.780672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:53.655269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:54.278954image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:54.955117image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:55.647377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:56.276662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:56.987799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:57.700854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:58.382538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:59.078672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:56:59.760895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:57:00.414260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:57:01.100347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:57:01.729700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:57:02.380702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:57:03.088804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:57:03.741028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:57:04.428192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-03-16T17:57:05.103782image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-03-16T17:58:06.246273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-16T17:58:06.631785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-16T17:58:07.139428image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-16T17:58:07.528388image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-16T17:58:07.893502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-16T17:57:12.726925image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-16T17:57:22.164721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

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Last rows

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